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 costume2:00 The three ways to judge Elizabeth Holmes2:50 Where we each land going in5:30 Theranos recap: the Enron dad, Stanford, the pivot9:00 Murdoch, the Waltons, and the $9 billion valuation11:00 The voice, the turtleneck, and fundraising as a woman14:40 No diligence: fake Pfizer reports, diluted blood, Walgreens16:50 The whistleblowers and what she was actually convicted of20:20 The documentary, part one: the month before prison and Billy Evans24:30 The economic upside question: did she know?27:00 Amanda Seyfried joins and wants to break them up29:30 Confronting Billy: power of attorney, the X account, the rage30:30 The kids, and whether we’re being manipulated31:50 Dead man switches and a gun33:30 From The Rehearsal to the reenactment35:20 The prison visit: two questions37:00 Did Nathan fall for her?39:20 Three things at the end: the contract, the swim, the New York Times quote42:00 Why Billy? Rich versus wealthy45:40 Does she believe her own lies? Who’s on first50:30 Figurative language and the medevac helicopter testimony56:30 Why Nathan Fielder? The “you picked me because I’m incompetent” confession59:30 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 agree1:06:40 ZIRP, AI promises, and what everybody does1:08:00 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

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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

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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