The Wall Street Skinny

Anthropic’s S-1 is finally public, giving investors their first real look under the hood of one of the most anticipated IPOs in years. We dig into the numbers behind the rumored $2 trillion valuation, Anthropic’s growth and profitability story, its massive AI compute commitments, and what the filing actually tells us about the economics of the business. But some of the most interesting details aren’t sitting neatly on the balance sheet. We break down the nearly $800 billion of disclosed commitments tied to compute and infrastructure, why Anthropic may need the public markets more than people realize, and the very unusual assumptions investors may have to make to justify a valuation this large. Plus, we get into what the S-1 still doesn’t tell us, and the numbers we’re watching as Anthropic gets closer to an IPO. Then we zoom out to what Anthropic’s IPO tells us about the broader AI trade: the neocloud selloff, CoreWeave, Meta’s latest capital-raising strategy, and the extraordinary amount of money required to fund the AI buildout. And because we can’t help ourselves, we get into Anthropic’s unusual share classes, the ticker $ANT, and why Instagram’s latest moves might tell us something much bigger about where Meta thinks the value of content really lies. 02:00 How Do You Value Anthropic at $2 Trillion? 03:00 Anthropic’s Business Model Is Changing 04:00 The Strange Metric Anthropic Wants Investors to Use 05:00 The Nearly $800 Billion Question 08:00 The AI & Neocloud Meltdown 10:00 Why Anthropic May NEED This IPO 11:00 Meta Is Raising Even More Money for AI 13:00 Anthropic’s Very Unusual Share Structure 15:00 Why Anthropic Chose the Ticker $ANT 17:00 Meta’s Latest Instagram Move 18:00 Is Content Creation Basically an MLM? 20:00 Why YouTube Pays and Instagram Doesn’t 22:00 The TikTokification of Instagram 24:00 How Social Media Algorithms Hook Creators 26:00 Want to See Someone’s Instagram? Unfollow Them 28:00 The Real Reason Meta Wants More Content

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Nathan Fielder’s You Can See Everything is the most talked-about documentary of the year, and after sitting through all three hours of it (there is exactly one acceptable bathroom break, and we tell you when), we came out more divided than when we went in. The film asks us to decide who Elizabeth Holmes actually is: a liar who knew the truth and deceived people anyway, a woman who lied to herself so thoroughly she no longer knows the difference, or a useful idiot who has been handed from one controlling man to the next. One of us walked in firmly in the second camp. The other changed her mind three times before breakfast. So we start by asking whether those are even the right three options.

To get there, we recap what Theranos actually was: the Enron connection, the Stanford dean who never bought it, Rupert Murdoch’s $125 million, the Pfizer logos pasted onto “independent” reports, and the diluted blood samples telling Walgreens customers they’d had miscarriages they hadn’t had. Then we walk through what Fielder does with it, and why the second half of the movie turns into something closer to a heist. Why does Amanda Seyfried go from hired reenactor to wanting to break up a relationship? What does Billy Evans have power of attorney over, and why does he carry a gun? Did Elizabeth know her boyfriend had negotiated a cut of the documentary when she looked into the camera and said she had no economic upside? And when two people watch the same scene of Nathan Fielder asking a convicted fraudster about the word “can,” how do they come away with opposite conclusions about whether she’s being literal or being clever?

That last question is where the two of us really split. One of us has known the other since age seven and has some things to say about how literal minds actually work, including a detour through medevac helicopters, SEC testimony, and whether a woman who quotes Yoda in her interviews gets to claim she doesn’t understand figurative language. The other wants to know why Adam Neumann, Elon Musk, and half of Silicon Valley never saw the inside of a courtroom for the same fake-it-till-you-make-it playbook. Is Elizabeth Holmes a mastermind, a product of the zero-rate era, or just the one who got caught? And after three hours of getting closer to her than anyone ever has, do we know more about her than we did before, or less?

Timestamps

0:00 We’re in costume
2:00 The three ways to judge Elizabeth Holmes
2:50 Where we each land going in
5:30 Theranos recap: the Enron dad, Stanford, the pivot
9:00 Murdoch, the Waltons, and the $9 billion valuation
11:00 The voice, the turtleneck, and fundraising as a woman
14:40 No diligence: fake Pfizer reports, diluted blood, Walgreens
16:50 The whistleblowers and what she was actually convicted of
20:20 The documentary, part one: the month before prison and Billy Evans
24:30 The economic upside question: did she know?
27:00 Amanda Seyfried joins and wants to break them up
29:30 Confronting Billy: power of attorney, the X account, the rage
30:30 The kids, and whether we’re being manipulated
31:50 Dead man switches and a gun
33:30 From The Rehearsal to the reenactment
35:20 The prison visit: two questions
37:00 Did Nathan fall for her?
39:20 Three things at the end: the contract, the swim, the New York Times quote
42:00 Why Billy? Rich versus wealthy
45:40 Does she believe her own lies? Who’s on first
50:30 Figurative language and the medevac helicopter testimony
56:30 Why Nathan Fielder? The “you picked me because I’m incompetent” confession
59:30 Will the public give her a second chance?
1:02:30 Is she any different from the rest of Silicon Valley?
1:04:50 Where we actually agree
1:06:40 ZIRP, AI promises, and what everybody does
1:08:00 Do we know less than when we started?

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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.

Short interest in the S&P 500 is sitting near its highest level since 2010, Jamie Dimon says he won't touch US stocks or Treasuries at these prices, and SpaceX is the ninth-most-shorted stock in the market ahead of its very first earnings report. So this week we're asking: why is everyone so bearish when the market is up more than 15% from the March lows? What did Alphabet's first-ever negative free cash flow quarter reveal about the real cost of the AI buildout? And when the five biggest hyperscalers are planning to spend nearly as much as the US military in a single year, who's actually going to fund it — and at what price? That last question took us straight to the bond market, where things look even scarier. Why are Meta, Oracle, and SpaceX's 30-year bonds trading 40-60 basis points wider just weeks after issuance? Why have 30-year Treasury yields held above 5.00% for the longest stretch in two decades — and is 5.00% the new floor instead of the ceiling? With a Fed meeting days away and Chairman Warsh's hawkish instincts colliding with the biggest negative CPI print since 2020, we dig into what the rates market is telling us about risk premiums across every asset class... and whether anyone wants to own anything right now.

We're talking about the buzziest --- and most controversial --- book of the summer: "Yesteryear" by Caro Claire Burke. It poses a question no one's brave enough to answer: are trad wives and career women fundamentally at odds? Or are they two sides of the same coin, minted by a bigger system that profits from their fight?

As two Wall Street veterans recently profiled in Bloomberg for our new career as "finance influencers", we HAD to talk about the novel everyone is calling "rage bait", and we've got quite a lot to say. Fair warning: we spoil everything, INCLUDING the plot twist that has readers and critics alike up in arms.

Why is a finance show covering a book about a trad wife influencer? The biggest names in this space, like Ballerina Farms and Nara Smith, are pulling in millions every year. The raw milk industry is a multi-billion dollar megalith expected to double in the next few years. And all of this is fueling a vertical of the creator economy that is growing in size (and scandals). What happens when a woman builds an empire by performing traditional acts of subservient domesticity on the most modern machine ever invented? We also turn the lens on ourselves, as moms, influencers, and educators --- where's the line between education and performance, and what responsibilities come along with influence?

Whether you loved this book, hated it, or refuse to read it on principle, we want to know what you think! Let us know in the comments...

With hyperscalers like Meta, Google, Amazon and SpaceXAI burning through cash, we decided to answer the question underneath all of it: what is this money actually buying? In this episode we start high level with a primer on the AI ecosystem or what Nvidia's CEO Jensen Huang calls the "five-layer cake" of AI — energy, chips, infrastructure, models, applications. We get into the vocabulary everyone uses and nobody defines: what a hyperscaler actually is, how it differs from a frontier model company like OpenAI or Anthropic, why Oracle only plays in one layer while Google plays in all five, and what a NeoCloud like CoreWeave is really doing when it borrows against its own chips. Then we get into the grid — all three of them — including how power prices get set, the difference between regulated and deregulated states, why Meta's $200 billion Project Hyperion campus in Louisiana needs enough electricity to power half of Manhattan in the summer, and why the new rule for data centers is essentially "bring your own electrons." We also dig into the tax incentives driving the timing of all this spend, and why states are competing so ferociously for projects that employ almost no one once the construction crews go home. Then we bring on an extra special guest: power expert. Ron Kelly, who spent 50 years in power and energy — as an engineer, at Calpine, and developing natural gas-fired power plants and solar plants all over the United States the country. He also happens to be Kristen's dad. His take is bracing: he's seen this movie before. Between 1995 and 2005, roughly 300 gigawatts of power projects were announced on the promise of the internet. 168 got built, 130 were canceled, the rest died, and Enron, Mirant, NRG, and Calpine all ended up in Chapter 11. Today's data center pipeline is about the same 300 gigawatts. Ron explains risks that could complicate the build out necessary to get all the needed power infrastructure online: the interconnection studies, transformer backlogs — plus what he really thinks about the security of the largest machine humans have ever built. Connect with Ron at / ronald-kelly-pe-mba-3587a718

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Mindy Kaling's new sitcom "Not Suitable for Work" just dropped, and we have thoughts. We are two Wall Street veterans breaking down everything the show gets right — and wrong — about what it actually looks like to show up as a first-year analyst at a bulge bracket investment bank, navigate office politics (and romance!), and try to build a life in New York City on a salary that sounds impressive until you see the rent.

But this episode goes way beyond the finance. We're digging into the bigger questions the show raises: Is the Gen Z "lazy" narrative fair, or are young people today actually working harder than any generation before them for a fraction of the opportunity? What does the clash between generations reveal about the tension between hustle culture and the new workplace? And when a show in 2025 depicts five young people meeting, dating, and falling for each other entirely without apps, is that wish fulfillment or an active campaign for something we've lost?

We're also getting into the male-female dynamics, the nepo baby problem, the intergenerational clash between millennials and Gen Z, and what it means that the most cutthroat character in the entire friend group is a woman. Plus — what does it say that the show's most pointed commentary on AI lands not in the banking storyline, but through a struggling med-student-turned-actor being asked to dig the grave of his own profession?

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While everyone's been fixated on the SpaceX IPO, Google quietly pulled off the largest equity offering in history—roughly $85 billion—and basically front-ran the entire market to do it. In this episode of The Skinny on Wall Street, Kristen and Jen break down why a cash-printing machine like Alphabet would raise money at all, and why they did it in the most fascinating way possible: a Berkshire Hathaway private placement at a discount, a common stock offering across Google's quirky three share classes, a $40 billion at-the-market program, and the structure that confuses almost everyone—the mandatory convertible.

If you've ever nodded along to "convertible debt" but secretly wondered what the hell stock that converts into stock actually is, this one's for you. Kristen (the First Lady of Valuation herself) walks through exactly how a mandatory convert works—why the number of shares you receive is a moving target tied to the share price, how the conversion math plays out from zero to a 25% premium and beyond, and why Google layered on a capped call to claw back even more upside. Along the way, they get into book-runner drama, IPO fee structures, why Tesla loved these trades, and what it really signals when sophisticated issuers are dumping rich equity, rich volatility, and rich call skew onto a market full of bullish retail buyers.

The bigger picture? This is the AI build-out narrative wearing a new outfit. With 100% CapEx deductibility on the table and a talent war driving nine-figure pay packages, the smart money is raising as much as it can, as fast as it can—and using the hype to do it on favorable terms. Tune in for a clear, no-jargon breakdown of one of the most interesting capital markets moves of the year. Want to go deeper? Check out our Investment Banking & Private Equity Fundamentals course taught by Kristen Kelley—20 years of Wall Street knowledge, yours for two years.

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We've done the finance of Industry, the finance of Succession, the finance of Belle Burden's Strangers — but we've never done the finance of CREATORS. So when Spotify invited us to their Investor Day, we knew we had to sit down and ask the question every aspiring musician, podcaster, and Instagram creator is obsessing over: in a world where everyone wants to be a creator, how does anyone actually get paid?

In this episode, we talk with Gustav Gyllenhammar, SVP of Markets and Subscriptions at Spotify, about the surprisingly complicated machinery behind every stream you play. Where does your $12.99 a month really go? How much does a million downloads of a song actually pay out? And how did a company born out of a piracy-ravaged Sweden convince an entire generation to start paying for something they'd grown up expecting for free? We get into the labels-versus-songwriters split, the rise of the independent artist, and the one number that explains why Spotify thinks it's playing a completely different game than the AI companies scraping the internet for content.

Which brings us to the real tension underneath it all: as LLMs hoover up the work of writers, musicians, and creators everywhere, who's building a model to actually compensate them — and is Spotify offering a better blueprint? We dig into Spotify's philosophy on AI, why they waited so long to touch it on the music side, what "Time Well Spent" means when every other platform is optimizing for your attention, and whether the creators who power these platforms are about to get boxed out of their own economy. Plus: the new Universal Music partnership, the audiobook feature Jen has been praying for, and why a direct listing might be the most underrated way to go public.

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In this episode, we dig into one of the biggest market questions hiding behind the hype around mega IPOs: what happens to passive index investors when companies like SpaceX, Anthropic, and OpenAI go public?

We ask why the VIX and major indices like the S&P 500 and Nasdaq look calm, while single-name stocks like Tesla are showing much higher implied volatility, and why the spread between index volatility and individual stock volatility has reached extreme levels. Along the way, we break down the dispersion trade, implied versus realized volatility, and whether upcoming IPOs could force investors to rotate out of existing AI, tech, and “Elon trade” names to fund new allocations.

We also explore how changing index rules could reshape the market structure itself. Should a massive company like SpaceX be included quickly in the Nasdaq or S&P 500? How do float requirements, seasoning periods, profitability screens, and liquidity constraints affect ETF investors and passive funds that have to buy the underlying shares? We debate whether excluding these mega-cap IPOs would distort benchmarks, whether including them could create liquidity pressure, and how SpaceX, Anthropic, and OpenAI could change the relationship between passive investing, active stock picking, and index volatility.

Finally, we ask whether today’s market setup is starting to echo the dot-com bubble, with bullish sentiment, a low put/call ratio, AI enthusiasm, and a wave of high-profile IPOs creating both opportunity and risk. Are investors buying call options like lottery tickets? Could the arrival of new public AI and space stocks drain capital from the Mag Seven, Tesla, software, and private markets? And as AI infrastructure companies become publicly investable, we question whether the real winners will be the foundational LLM providers, the tech giants, or the next generation of startups built on top of them.

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