The $1.75T IPO No One Can Price — 6 Things That Surprised Us This Week
Jack Forehead and Matt Zigler run a tightened, six-clip "things that surprised us" wrap, anchored by the **SpaceX IPO arriving at roughly 100x sales for a ~$1.75 trillion market cap** (Matt: "between one and a half and two depending on what happens at the open"). Cameron Dawson frames the valuation against Palantir — the priciest S&P 500 name at ~65x sales today, ~$340B — and warns that even when great news arrives at such multiples, stocks "hit a ceiling," though Musk's name (Tesla's forward earnings down ~two-thirds since 2022 while its multiple climbed past 100x) shows fundamentals can stop mattering. The hosts' sharpest contribution is the **market-structure / flows problem**, via ETF veteran Dave Nadig: a company this big with a tiny ~$75B float, accelerated index inclusion, NASDAQ-100 buying ~15 days post-IPO, perps already pricing it offshore, and options three days after — "there is no real price discovery" for at least six months, and "I don't think that there is an edge to be had here." S&P refused to waive its seasoning/profitability rules, so SpaceX won't enter the S&P 500 soon; **free-float adjustment means a notional 5%+ market-cap weight collapses to ~0.1% in many indexes** — and the flows logic extends to the coming OpenAI and Anthropic IPOs. The second thread is **Kai Wu's reframing of value's "death"**: split the market into technologically exposed vs. insulated industries, and value works fine in insulated sectors — the entire factor drawdown comes from exposed ones (retail, software), and that loss overwhelms the rest. Wu's dispersion work adds that disruption-scare stocks have fat tails — ~10% double over the next year vs ~3% market-wide, ~16% halve vs ~7% — making this potentially a career-best setup for elite software pickers (and a graveyard for the wrong ones). Jim Paulsen flags **leadership rotating to unprofitable small-cap tech beating the Mag 7**, the Goldman Sachs AI index re-rating from ~35x to over 70x earnings since March 30, and the historical pattern that **most equity pain comes after oil peaks, not during its rise**. Builder takeaway: the AI-IPO wave's mechanics, not its narratives, will set near-term prices.
Key points
- SpaceX is set to IPO at roughly 100x sales for a ~$1.75 trillion valuation (range $1.5T–$2T at the open) — a multiple normally seen only on far smaller companies, with the reported TAM cited loosely as ~$27 trillion.
- Cameron Dawson's Palantir benchmark: the most expensive S&P 500 name on price-to-sales (~65x now, ~$340B) re-rated from ~90–96x sales then delivered 72% sales growth in 2026, yet the stock stalled — high multiples cap upside even when growth arrives.
- The 'Muskian' caveat: Tesla's 12-month forward earnings power fell by nearly two-thirds since 2022 while its multiple rose above 100x earnings, because the stock kept rising — fundamentals can stop mattering for Musk-narrated names.
- Dave Nadig on market structure: a ~$75B float, accelerated index inclusion, NASDAQ-100 forced buying ~15 days post-IPO, offshore perps already price-setting, and options three days in mean ~30 days of unpredictable supply/demand and no real price discovery for up to six months.
- S&P Dow Jones declined to waive seasoning (would not cut 12 months to 6) or the profitability requirement, so SpaceX will not enter the S&P 500 for a long time; NASDAQ/Russell indices are changing rules and using a multiplier to include it.
- Free-float adjustment is the underappreciated detail: a notional 5%+ market-cap weight shrinks to roughly a tenth of that — way sub-1%, often ~0.1% — so index funds barely move; but thin float also means active managers fighting over little supply.
- The same flow dynamics will define the upcoming OpenAI and Anthropic IPOs — lumpy, forced flows running counter to price discovery, a 'defining moment' for this year's AI-IPO wave.
- Kai Wu's key finding: split the market into technologically exposed vs. insulated industries — value works fine in insulated sectors (no real change since 2010), and the entire net-negative value-factor return comes from the drawdown in exposed sectors like retail and software.
- Wu's dispersion result: disruption-scare stocks (exposed sector + trailing-12-month 30% loss) have fat-tailed forward returns — ~10% double in the next year vs ~3% market-wide, ~16% lose more than half vs ~7% — same median (~6–7%) but far wider spread, a setup that can vindicate or destroy active managers.
- Jim Paulsen on leadership change: Russell 2000 small-cap tech and unprofitable tech are now beating the Mag 7, the Goldman Sachs AI beneficiaries index ran from ~35x to over 70x earnings since end of March, signalling a riskier, late-1990s-style rally with new drivers.
- Paulsen's oil pattern: across peaks back to 1970, most negative pressure on stocks and the economy arrives after oil peaks, not during its rise — March 30 (Trump/Iran both blinking, oil having run to ~$100–$120) sent the market 'straight north,' but the historical risk is in the aftermath.
Notable quotes
So SpaceX is going to come out at 100 times sales and it's going to be what, like 1.75 trillion, give or take, is that right?
Yeah, between one and a half and two depending on what happens at the open.
So it's a word of, not necessarily caution but like historical wisdom just to say when you trade at such a high multiple, even if the great news happens, what tends to happen is you kind of hit a ceiling mostly of a stock of that size.
So you've seen the 12 month forward earnings power for Tesla since 2022 fall by nearly two thirds in that, in that time.
Just the trading widget that we're going to move around is going to have bizarre and I would say very unpredictable supply and demand components for at least the first 30 days.
I don't think that there is an edge to be had here.
And it's going to be like less than a tenth of that because of the free float adjustment.
This is going to have implications on anthropic and OpenAI and whatever comes after.
In other words, values work just fine as long as it's not in industries that are exposed to technological disruption.
Goldman Sachs AI beneficiaries index has gone from like 35 times earnings to over 70 times earnings just since the end of March.
Themes
- AI-IPO-wave mechanics
- index-inclusion and free-float flows
- value-factor revival
- market-leadership rotation
- AI-capex valuation re-rating
Mentioned
People
Companies
Ideas
- price-to-sales multiple
- free-float adjustment
- index inclusion mechanics
- price discovery vs forced flows
- value factor death and revival
- technologically exposed vs insulated industries
- return dispersion and fat tails
- market leadership rotation
- unprofitable small-cap tech leadership
- oil peak as equity-market signal
- too-hard pile