Valuations not so crazy after all?

• Public markets keep breaking all-time-highs and the doomers are hollering “bubble.” If you look at p/e ratios, you might also notice that investors are paying historically high prices for earnings. So, checkmate for the no-bubble bros, right?

• Not so fast. It’s true that multiples on earnings are historically high, but it’s also true that companies are historically profitable–and valuations have scaled accordingly. The higher the margins, the higher the p/e ratio.

• At ~14.5% margins, the S&P is more profitable than ever. And unlike the clear “bubbles” of Dotcom (orange) and Pandemania (dark green), current valuations are well within trend. Adjusting for margins, the current p/e is ~17.75%, which is about the same as it was from 2015-2018!

The point here is that for all the histrionics, investors aren’t dumb. There’s a clear method to the madness, and it’s all about profitability. Fat margins and growing profits lead to high stock prices, and fat margins and growing profits is exactly what PublicCos keep doing.

Now, reasonable minds can disagree about whether forward earnings estimates are correct (and there are of course many other things that affect stock prices), but taking them at face value, there is nothing bubbly at all about current valuations.

US Startups doing it faster

• Stripe’s data shows that US startups are growing revenues much more quickly than the rest of the world. US startups have grown ~1400%, while EU and UK startups lag far behind at ~600% and 500% respectively.

• It’s not AI-either. When you strip out AI, US startups still have a pretty wide lead. Although, interestingly enough, AI startups did start to pull away from non-AI, sometime around 2024.

Why is growth so much faster in the US? It’s hard to say for sure, and it’s probably not one reason.

Patrick Collison thinks it might have something to do with technological adoption: US startups adopt new tech more quickly than most. Other reasons might include regulation (Europe has a lot), market-size (US is bigger), and/or the spending appetite (and tech adoption) of US customers. In all events, US startups are absolutely cooking, so ‘Fade America’ at your peril.

Survey says: gimme more AI

• Speaking of revenue growth, ~88% of enterprise decision-makers anticipate spending more on AI over the next 12 months, according to a recent Wharton Survey. That’s a 16pp increase from last year, or +22%.

• The biggest shifts in “substantial” spending increases are in IT, Operations, and Finance/Accounting, where ~25% of respondents expect a lot more to come. Legal remains the relative laggard, but even there, a majority of respondents expect AI spend to increase.

• Also of note: the number of respondents expecting to decrease spend is barely visible. That’s consistent with the wildly impressive retention curves, recently highlighted by Ramp’s spending data: AI retention curves for business customers have improved every year, with current 9-month retention just under 90%.

This one pretty much speaks for itself. Sure, it’s only a survey, but expectations are that enterprises are going to keep investing in AI, and even more so than before (and basically no one said “less”). ROI may be hard to measure, but we’re two years in and businesses like what they see, more and more. We’re still in the very early innings of this thing.

Forward-deployed payback goes parabolic

• Palantir PLTR 0.00%↑ reported earnings this week, which included a “staggering” $476M in profit for the quarter.

• That’s ~3.5x the profit from just a year ago, and as Mr. Karp is wont to remind us, that means the business is now generating “more profit in a single quarter than it did in revenue not long ago.”

There’s nothing quite like a Palantir letter to shareholders. The more interesting thing about all this profitability (besides the passing reference to Yeats) is that Palantir pioneered the so-called “forward-deployed engineer,” a hybrid software and services approach to selling its wares.

The question, of course, was whether Palantir could achieve software-like profitability, with its more labor-intensive approach. The answer, for now, is “yes,” especially when it comes to the commercial application of LLMs. Karp credits the give and take between software and engineer as charting “the route for . . . something universal—a generalizable artificial intelligence platform—[] made possible by an obsessive focus on the specific: the particularized and quite idiosyncratic challenges and technical problems of our disparate set of customers.”

You do you, Mr. Karp. We’re only here to observe that whatever it is you’re doing, the forward-deployed model appears to be working.

SF (office) is so back

• For the past 4 years, demand for SF office space had fallen off a cliff, running ~50% below pre-pandemic levels, according to data from VTS.

• In September, everything changed: SF office demand skyrocketed to ~25% higher than before-times.

• The burst of office demand isn’t a nationwide thing, either: all the other metros are running about where they were before, including the new poster-children for office malaise, D.C. and Chicago (where demand is still less than half of the status quo ante).

SF is back. They said it couldn’t be done.

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