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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

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. How do Bitcoin miners end up running the infrastructure behind AI? This week we break down neoclouds like CoreWeave, Nebius, and Lambda. What makes a neocloud different from AWS, Google Cloud, Azure, and the hyperscalers? Why were crypto miners, sitting on warehouses, power contracts, and a business model with a built-in expiration date, so well placed to pivot? And if these companies don’t have the cash flows of a Google or a Microsoft, how are they paying for billions of dollars of chips? We dig into chip-backed financing. How can a junk-rated company borrow against Microsoft’s AAA credit? Why are lenders comfortable with 95% advance rates? What happens when the compute contracts are only three to five years long and customers like OpenAI aren’t profitable yet? Then we get to the NVIDIA of it all. NVIDIA invests in the neoclouds, backstops their unused capacity, and sells them the chips. Is that savvy, or is it circular financing? How does it compare with Lucent in the dot-com bust? What about the take-or-pay obligations that never show up on anyone’s balance sheet? Plus: OpenAI’s revenue miss, how media bias shaped the coverage, and whether Starbucks and Chipotle should merge (pumpkin spice burrito, anyone?). 0:00 Market-Moving Headlines: OpenAI’s Revenue Miss 3:27 Starbucks & Chipotle M&A 6:29 What Are Neoclouds? 9:12 From Crypto Miners to Neoclouds 13:40 Hyperscalers vs. Neoclouds 17:21 Chip-Backed Financing 23:30 The NVIDIA of It All 29:56 Circular Financing Risks

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. We're back at the intersection of AI and high finance! Anthropic is reportedly targeting a $2 trillion IPO valuation. So what did Reuters find when it got an early look at the S-1? Kristen, our resident investment banker, walks through the numbers: $4.6 billion in 2025 revenue, a $42 billion loss (and how much of it was an accounting charge on a convert), $20 billion in cash against a reported $518 billion compute plan, and a quarter of revenue coming from just two customers. Why don't those compute contracts show up as debt when the neoclouds on the other side are borrowing against them? And with open-weight models closing the gap, is it time to get nervous about this IPO? Then we turn to the off switch. After a summer of OpenAI agents breaking out of the sandbox, a paused model, and a scrapped GPT-6.1 Astra release, who is actually reaching for the brakes? We cover Dario Amodei's "We Must Pace the Frontier" essay and the cynical read on it, the AI Kill Switch Act and the AI Emergency Button Act stalling in Congress, the executive order rebranding AI as "super intelligence," and the White House Accord on Super Intelligence, typo and all. And why did the industry name its newest products after the villains in The Matrix? Finally, NVIDIA swore off kill switches in 2025, citing a 1993 government fiasco called the Clipper Chip. So why did it just announce a two-part system called OpenShell and Sentry, and what would it catch inside a private equity data room or on a trading desk? Can any guardrail keep up with something built to be smarter than us? Plus the trolley problem, Iain Banks' Culture series, and whether we should just give up and let the robots win. Make sure you're subscribed to our Substack so you don't miss our more in-depth coverage on these complex topics: https://substack.com/@thewallstreetskinny

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. NVIDIA just agreed to buy Hugging Face, and the deal won't add a cent to its earnings per share. So why do it? In this episode we dig into what NVIDIA really gets from owning the home of open-weight AI, and whether this is a threat to OpenAI and Anthropic that the market hasn't priced in yet. What's the actual difference between open source and open weight, and why does almost everyone (yes, even us included) mix them up? If Amazon, Google, and the frontier labs are all building their own inference chips, where does NVIDIA's next wave of customers come from? And if the free "Temu version" of AI gets good enough for most people, who keeps paying for the Prada bag? Then we bring on Michelle Weaver, Head of US Thematic Equity Research at Morgan Stanley, to ask the questions everyone in finance is worried about. Which companies are actually seeing AI show up in their margins, and which are just burning tokens so they can say "AI" on the earnings call? Is AI already showing up in the jobs data for young workers? What happens to the traditional Wall Street analyst class if the org chart stops being a pyramid? Does the Jevons Paradox of economic theory help or hurt the frontier model companies? And why are American companies nervous about building on Chinese open-weight models? We also get into the growing fight over AI data centers. Why has this become one of the few issues uniting voters on both the left and the right? What do power bills, water use, and old brownfield sites have to do with it? How big is a 57-gigawatt power shortfall, really? And what does Oracle declaring force majeure on its New Mexico Stargate site tell us about where the AI build-out is headed? 00:00 Intro: Revisiting our early episodes 03:00 Life updates: Flying, dogs & kids 06:10 Our Yahoo Finance Market Hang recap 07:50 Why NVIDIA bought Hugging Face 11:10 Training vs. inference: Where AI compute is headed 14:15 CUDA and NVIDIA's software moat 15:50 Open source vs. open weight, explained 19:20 Is NVIDIA a threat to OpenAI & Anthropic? 21:25 Meta's Muse and why easy AI wins 25:00 Why companies struggle with AI adoption 27:20 Oracle's Stargate force majeure (Ground News) 28:45 Michelle Weaver joins: What is thematic research? 30:15 AI enablers vs. AI adopters 35:05 Open vs. closed models and Jevons paradox 37:25 The risks of Chinese open-weight models 39:45 Pricing security risk in AI adoption 42:15 Token budgets and the end of token maxing 44:00 Is AI already costing jobs? 46:55 What AI means for Wall Street analysts 49:25 Data centers: People, power & politics 53:00 Tax breaks, brownfields & state moratoriums 55:35 The 57-gigawatt power shortfall 57:35 US vs. China: Chips, power & the AI race 59:20 Where to find Michelle's research

Go to www.groundnews.com/skinny for a better way to stay informed. Subscribe through our link for 40% off unlimited access to world-wide coverage. The first Fed rate hike since the summer of 2023 hit today, and we went LIVE from New York City to cover it. Minutes after Chairman Warsh wrapped his press conference, we brought on Wellington's macro strategist Mike Medeiros to make sense of the 25bp hike, the unanimous vote, and a statement that leaned unmistakably hawkish. We asked Mike everything we've been dying to know: Was this hike a nod to the political pressure campaign coming from President Trump, or would cutting in this environment have been, in his words, "clinically insane"? Are Warsh and Treasury Secretary Bessent working together or against each other, with the Fed pushing financial conditions tighter while Treasury intervenes in the long end? How does Treasury actually fund those interventions next year — bills, buybacks, or the TGA — and does the debt ceiling take one of those off the table? We also dug into what the hike means for the coming wave of corporate issuance financing the AI buildout, whether the midterms change the policy calculus, where positioning is most offsides, his S&P target for year-end, and what the death of forward guidance means for volatility in the front end of the curve. Plus: what nickname does Trump give "No Cut Kevin" now? Thank you to our sponsor, Ground News — our go-to news aggregator for getting all the headlines moving markets in real time, gauging bias across the political spectrum, and seeing what's factually accurate reporting and what isn't. Go to https://groundnews.com/skinny to subscribe for 40% off their unlimited access Vantage subscription — whether you subscribe yourself or send it as a gift — and use our link so they know we sent you.

Everyone in the financial media is telling you the same story: Scott Bessent's surprise Treasury buyback announcement is a thumb on the scale of the bond market, and it's making Kevin Warsh's job at the Fed harder. But what if the headlines have it exactly backwards? What if the Treasury Secretary is actually running interference for the one thing the Fed Chair can't do himself? Fresh off asking Bessent this question face-to-face at the Charlotte Economic Club, Jen breaks down why she thinks there's a stealth rate hike hiding inside what looks like yield suppression, and why the best way to understand it is a three-act magic trick straight out of The Prestige.

To get there, we have to answer the questions most coverage skips entirely. What actually happens at a quarterly refunding, and why does the Treasury auction schedule matter more than almost anyone realizes? What's the difference between an on-the-run and off-the-run bond, and why are aged long bonds from the COVID era trading below 50 cents on the dollar? When the Treasury buys back 30-year paper and funds it with T-bills and short-dated notes, is that QE, or is it something closer to the opposite? And why would Bessent make this move right before the AI hyperscalers flood the corporate bond market in September?

Then things get weirder. Why did the US just participate in the biggest coordinated yen intervention in decades, and what does it have to do with Japan's Treasury holdings? How do stablecoins, a resurgent crypto market, and a shaky dollar all fit into the same trade? And what do Bessent and Warsh, supposedly at war, have in common through their shared mentor Stan Druckenmiller, whose AI-generated op-ed became its own scandal?

The LA Lakers --- a prestigious basketball franchise that didn't change hands for 46 years --- just sold twice in ten months, the second time for $2.5 billion more than the first. The seller is Mark Walter, CEO of Guggenheim, owner of the Dodgers and part of Chelsea FC, and the man whose laptop and phone the FBI seized off his private jet just as the Lakers deal was coming together. Since then, one of the insurance companies in his orbit has revised its reported related-party investments from $1.4 billion to $17 billion. Quite the rounding error. In this episode, we get into the questions everyone on the Street is suddenly asking. Why would anyone sell the crown jewel of American sports a year after fighting to buy it? What do a bunch of boring life insurance companies most people have never heard of have to do with the trophy assets of the billionaire class? Why do private equity and private credit firms keep buying insurers in the first place — and what happens to that entire model when the disclosures around "related parties" turn out to be, let's say, incomplete? And why did Josh Kushner and Bob Iger, who spent months chasing a Las Vegas expansion team, pivot to the Lakers over a single weekend? The bigger question hanging over all of it: this structure — asset managers selling their own loans to insurance companies they control — underpins a massive share of the boom in private credit. If regulators start pulling on this thread, how many other portfolios look like this one? By way of disclaimer, no charges have been filed and no wrongdoing has been established nor is implied here; our research merely summarizes reporting from Bloomberg, the FT, the WSJ, and the LA Times.

We got to sit down with John Quinn — the "Quinn" of Quinn Emanuel — one of the most powerful litigators in the world, and the man whose firm is defending OpenAI right now. What does someone at the very top of the legal food chain see coming that the rest of us don't? And why does he think one of the oldest concepts in law is about to become the most valuable weapon in tech? Here's what pulled us down this rabbit hole. The most valuable companies on earth are pouring hundreds of billions into inventions almost none of them can legally protect. So how do you build a moat around something you're not allowed to own? Why are Apple and OpenAI trying to destroy each other in one courtroom and sitting on the same side in another? And what does any of this have to do with a viral park bench kiss that blew up one of the big law talent poaching deals? We get into all of it: why the patent wars fell by the wayside, what actually replaced them, and why every giant pay package in tech, finance, and now law is really a fight over the one thing no company can lock in a vault. Then John tells us where he thinks this is all headed — for the AI giants, for the lawyers, and for anyone thinking about the profession at all.

After the largest IPO in history, only about 5% of SpaceX — roughly $83 billion of the $1.75 trillion — is actually free to trade. Insiders are locked up, the banks that underwrote the deal can't lend shares to short sellers, and index funds are being forced to buy as SpaceX joins the Nasdaq-100 and the Russel. In this episode, Jen and Kristen, both former Morgan Stanley investment bankers, break down how the IPO was engineered — and the question every SpaceX investor should be asking: what happens when all that locked-up stock can finally sell? First, we cover what is normal in an IPO so you can see what isn't. We cover price talk vs. the $135 take-it-or-leave-it pricing, the green shoe, perpetual futures, and the fast-track Nasdaq-100 inclusion pulling in billions of passive buying. We lay out the risks, meaning the the wall of supply coming. Unlike the standard 180-day lockup, SpaceX is staggering its release: the first ~$240-500+ billion of stock unlocks after the first earnings report around September, with more tranches every few weeks after that — over $1 trillion freely tradeable by December, on the way to a ~$2 trillion overhang once Elon Musk's one-year lockup rolls off. But we also lay out why the passive buying actually helps dampen that supply PLUS why many institutional investors are NOT bearish on the stock despite the insane valuation.

What took Situational Awareness from a $45bn hedge fund down to a $10bn hedge fund in less than a month? Two years ago Leopold Aschenbrenner was a researcher at OpenAI who wrote a 165-page essay about superintelligence. Since then, he raised $225 million seed funding from Stripe co-founders, Jane Street, and GitHub's CEO, which he proceeded to turn into an AI hedge fund called Situational Awareness worth about $45bn as of the beginning of July. He did this with no prior trading experience, 4-5x leverage on a concentrated bet in AI names. By Thursday the fund was down to about $10 billion. Neither Millennium nor Jane Street were willing to step in to catch a falling knife. Ultimately Citadel stepped in to buy the flagging portfolio. Here is the crazy part though: Aschenbrenner wasn't wrong. He is reportedly still up around 80% on the year and "he only sold enough to cover his losses". But what caused a massive drop in the global markets was that a prime broker does not care what happens in 2030. And because half the market was crowded into the exact same names, his exit was everyone else's problem. SK Hynix and CoreWeave cratered. Korea's Kospi tripped circuit breakers. Over a million retail accounts got margin called. All of July's violence, the moves that had traders questioning their own sanity, was one book being taken apart in public. So the question this episode actually asks is whether this was one overlevered fund or the first crack in the AI trade itself. Because the market's answer this week was a shrug. Microsoft just posted the largest single-day market cap gain in history and credit spreads snapped back tighter, as if the whole thing was somebody else's accident. Kristen and Jen have both traded through cycles that ended this way, and they have seen exactly how comforting that shrug feels right before it stops being true.

*Note: This episode was recorded before news broke of Situational Awareness' unwind, which gives us much better insight into the pace and magnitude of the move in the Korean markets specifically.* Chips, China, and credit. The three forces tearing through the AI trade right now, and we called it last week. In this episode we break down why the bond market cracked first, what widening credit spreads on Nvidia, Meta, and Oracle are actually telling you about default risk, and why the Nasdaq is bleeding while the S&P barely flinches. We walk through Alphabet's first negative free cash flow after twenty years of printing money, the CapEx numbers that keep getting revised upward, and the moment the market stopped rewarding spending and started punishing it. If you have ever wondered how to read a credit spread, we show you the math live. Then we get into the China story that moved markets this week. CXMT went public in the largest mainland Chinese semiconductor IPO on record, oversubscribed 212 times, and the Korean stock exchange took the hit because the KOSPI is essentially a memory-chip index wearing a trench coat. We explain why memory matters in an AI data center, why Samsung, SK Hynix, and Micron controlling 90 percent of the market was the whole moat, and what reports of domestically produced DUV lithography machines would mean for US export controls. We also unpack Nvidia guaranteeing borrowing for a 10-gigawatt OpenAI data center in Ohio, and whether circular financing between chipmakers and model labs is clever structuring or an accounting Ouroboros. Finally, the philosophical hangover. We react to Elon Musk's Economist interview and his claim that money stops mattering within a decade, pressure-test his deflationary argument against MV equals PQ, and ask why every science fiction author who ever imagined artificial superintelligence wrote a horror story. Plus Anthropic's positioning ahead of a possible IPO, the distillation and copyright fight with publishers, the rare books being unbound and shredded to feed training data, and where value actually accrues if models commoditize. Energy and molecules, or something else entirely. Subscribe for weekly deep dives on AI infrastructure, credit markets, semiconductors, and the money moving underneath the entire AI build-out.