Why Every Hype Cycle Produces the Same 23 Roles

The structural argument

Every emerging technology ecosystem requires a predictable set of functions to mature. Someone must build the foundational technology. Someone must commercialize it into products that non-technical buyers will actually pay for. Someone must explain it to the people who don't have time to read the whitepaper — and evaluate it critically for the ones who do. Someone must fund the whole thing and connect the people who otherwise wouldn't find each other. Someone must negotiate the technology's relationship with existing legal and regulatory structures. And someone must provide the infrastructure on which everyone else runs — and profit from the price inefficiencies that immature markets always contain.

None of these functions are optional. An ecosystem without builders doesn't scale beyond a research paper. Without educators, adoption stays niche. Without capital, promising ideas die in the trough of disillusionment. Without legal navigation, promising ecosystems get shut down before they prove their value.

7 categories. 23 roles.

The 23 roles across 7 categories aren't arbitrary — they map to six functional necessities every cycle has to fill, plus a seventh figure, the Charlatan, that every cycle produces but nobody should aim to become:

Bitcoin, AI, Linux, 3D printing — same cast, different costumes

Consider the Tech Genius role. In the Bitcoin cycle, it's Satoshi Nakamoto and Vitalik Buterin — people who invented the foundational protocol. In the AI cycle, it's the authors of the Transformer paper and Ilya Sutskever. In the Linux/open-source wave, it was Linus Torvalds and Richard Stallman — the kernel and the GPL everything else stands on. And in the desktop 3D-printing wave it was Adrian Bowyer, whose open-source RepRap — a printer that prints its own parts, released under the GPL — was the hardware equivalent of the kernel. The role is identical; the names changed.

The Educator in the Bitcoin era was Andreas Antonopoulos — making cryptographic concepts accessible on YouTube before anyone else thought it was worth doing. In the AI era, it's Andrej Karpathy doing the same thing for neural networks. In the Linux wave it was the Linux Documentation Project and Eric S. Raymond's essays, making open source legible to a generation of engineers. In the 3D-printing wave it was Make: magazine, Maker Faire, and YouTubers like Thomas Sanladerer teaching a generation how to print. The monetization path (audience → reputation → courses, speaking, consulting) is structurally identical.

The Infrastructure Landlord in Bitcoin was Bitmain selling ASICs — whoever controlled the hardware controlled the margins. In the AI cycle, it's CoreWeave and Lambda Labs selling GPU compute. In the Linux wave it was Red Hat, charging a subscription for supported Linux — the “rent” on free software. In 3D printing it was Stratasys and 3D Systems — and the filament makers, who sell the razor blades for a cheap razor. The insight is the same: in any gold rush, sell shovels.

Linux matters here for a specific reason: it's the oldest complete public-tech cycle on record, and the one many experienced engineers actually lived through. It had a real peak and bust (the 1999 Red Hat and VA Linux IPOs, then the 2001 dot-com crash) and a thirty-year compounding tail ending in IBM's $34B Red Hat acquisition — with the lowest charlatan density of any cycle in the book, because open code makes faking competence hard. It's the cleanest proof that these roles describe opportunity structure, not hype.

3D printing matters here for a different reason: it's the proof the roles aren't a software artifact. It is a hardware cycle that ran the full software-cycle script — open-source origins (RepRap), a hype founder who closed his designs and sold at the peak (MakerBot to Stratasys, up to $604M in 2013), a stock bubble that burst on schedule (3D Systems −46% in 2014), and a second wave that revived it (Prusa, Bambu Lab). It even forced the Regulator into being physically: when Defense Distributed printed the first working gun, the US State Department pulled the files under arms-export law within days. The two questions this book separates — is it just hype? and who gets hurt? — were both asked in this one cycle.

The pattern holds across every category and every cycle. The specific technologies differ. The roles don't.

Why most people pick the wrong role

Most people default into a role by identity rather than by fit. Someone who writes code becomes a Developer by default — even if their actual comparative advantage is explaining things (Educator) or building small autonomous products (Micro-Founder). Someone with a law degree becomes a Lawyer — even if their network and relationship skills are the real asset (Connector or Community Organizer).

The mismatch is costly because the roles have very different requirements for pain tolerance, capital, time horizon, and skill profile. A Scaler-type who tries to operate as a Micro-Founder will be bored and undercapitalize. A Freelancer-type who tries to build a VC-backed startup will resent the loss of autonomy. A Capital Provider who can't stomach the variance will exit at the bottom.

The assessment doesn't ask which role sounds good. It measures which one fits — based on what you can actually tolerate, what energizes you versus drains you, and what you've built so far.