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Some of the biggest peaks in equity valuations have been driven by bets on new technologies: 2021 and 2000 were online-inflected software bubbles with different details, the late 1960s were a tech bubble driven partly by defense spending and partly by optimism about dual-use products, including computers. 1929 was the everything-tech peak: electricity, cars, planes, financial engineering. 1901 had its own proto-tech boom, also driven by a mix of genuine technological improvements (like deflation in steel prices unlocking new use cases) as well as M&A, and several equity spurts in the late 19th century were driven by different iterations of a big bet on railroads.

If you talked to an investor at any of those market peaks, and showed them a preview of the economy twenty years later, they'd be thrilled that they'd made exactly the right call, at least if they looked at total freight-ton miles and not unit prices. A bull who bought the big trusts at the peak in 1901 would be happy at how high industrial production had gotten, but disappointed that the growth businesses they'd bought evolved into cyclical, low-value added suppliers to big growth companies. And someone who had fully-backed those growth companies circa 1929 would be thrilled to learn how significant radio was two decades later, how ubiquitous electrification had gotten for both homes and manufacturers, and how much the promise of Dow 380 had been fulfilled in America's workplaces and living rooms. The fact that the Dow averaged half that level in 1949 would have been deeply confusing to them.

The similar stories you can tell about the 60s, 90s, and 2020s peaks are all pretty familiar, but it's fun to take the most recent one seriously. An investor going max long tech stocks in late 2021 was incredibly optimistic about software's ability to wrap itself around a growing number of economically-valuable tasks, and to gradually expand the revenue surface area, so more of the world's economic activity would be mediated through software, and more of the resulting upside would be captured by software.

Which was absolutely right! We have more apps than ever, and they're incredibly good at price-discriminating their way into capturing maximum revenue. Not only that, but one part of the margin-expansion story is working—it takes a lower developer headcount than ever to support a given level of revenue. Just not in the way someone buying Bill.com at 100x trailing revenue would have hoped.

In a way, this pattern has to be universal: if investors, in the aggregate, were always able to identify not just which tech trends mattered but which layers of the stack would collect the most value, markets would have to be pretty close to perfectly efficient all along. But a tech-driven boom is an exploratory process for technological capabilities, the most cost-effective way to deliver them, and the optimal way to charge for them. If there's any given piece of infrastructure that's a useful complement to some other layer that has more pricing power, then overinvestment in that infrastructure acts as a subsidy for whoever controls the real value lever: companies racing to lay fiber in the late 90s didn't have a good way to mark their fiber up more than that of competitors, whereas online services varied quite substantially in how well they could monetize a given terabit of information. As long as there's any complementarity between different bets on the same tech theme, and any uncertainty about which categories will do well, this kind of dispersion will show up. Sometimes, it's temporary; in 2023, companies used to be able to engineer a stock rally just by announcing that they were using some widely-available model, but that quickly became a low-class move. Sometimes, it takes longer; in the aftermath of the dot-com bust, one sophisticated take on eBay was that they'd avoided making the mistake of owning a high fixed-cost delivery network. (As it turns out, they were a bet that bandwidth for packets mattered more than bandwidth for packages, and as packets sped up, the most important source of latency turned out to be the one they didn't control.)

There are also cases where not only is the winning technology identified early, but the winning category is, too. You could have made a bet early that socializing would move online, especially once people carried around Internet-connected phones with cameras. But you could have gotten that wrong by betting on whichever system was the most open (MySpace, accidentally), or with the biggest parent company (Google+), or with the best adoption among the chattiest nodes in the world's communications network (Twitter, Snapchat). The runaway winner, Meta, doesn't have some simple shorthand to describe what they got strategically right (maybe the closest is “real identities on the internet”); but they were good at continuously understanding what other services got right and then implementing it themselves.

It's handwavy to say that "execution" is what matters, especially since execution is mostly measurable in retrospect, and the way to measure it is to look at who ended up winning. And, even then, "execution" tends to talk about a snapshot in time: the early automakers had to have incredibly efficient manufacturing, and were complex enough that they helped spur the development of modern corporate organization and accounting. But the insights they had in the twenties through the fifties did not carry over into making them agile enough to survive in the globally-competitive, efficiency-focused auto industry of the 70s and onward. It took some great execution to get to the point where they could decline so far and still survive.

Ultimately, what all of this illustrates is that it's hard to make good macro bets at a distance. If you're in the middle of the bubble, you can have some sense of what behavior is mystifying until you hear that investors like it, and what's valuable and hard to replicate. On the other hand, it's a pretty hefty upfront due diligence commitment to get good enough to get the Anthropic offer as a precondition to figuring out whether value accrues to Anthropic, its customers, its suppliers, or someone else. But, in the end, that's just another kind of market efficiency: some trends in the efficiency of specific technologies can be underwritten early. And if it were easy to extrapolate from that to knowing exactly which stocks to own, markets would be a whole lot less volatile and more efficient.

The Diff tries to look at both broad technological themes and the narrow differences that differentiate winners and losers. Consider:

• The varieties of drawdowns.

• How Bubbles and Megaprojects Parallelize Innovation, a 2020 piece that illustrates its point by noting that Nvidia’s rally to that point looks less vertiginous if you consider new GPT-wrapper startups.

• How the car industry manufactured the modern middle class ($).

• Electrocloud! Or, how the rollout of cloud computing mimics some patterns from the history of electrification.

• A perversely good example of execution trumping the cycle is the story of how Teledyne navigated the bursting of the electronics bubble ($).

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1  Those famously florid profile pages were the result of improperly sanitizing inputs into other profile fields. It turned out that the most appealing designers for teenagers' homepages were not the twentysomethings who worked at a slightly dodgy Internet conglomerate.