I've always thought Allison would be a great VC. This is awesome!

Hardware is hard

Excited to share that I've stepped into a new role as Head of @MARAFoundation @MARA MARA Foundation represents the company's ongoing strategic commitment to Bitcoin—and our belief that Bitcoin is the most important emerging technology for self-sovereignty and human freedom in the world. Through internal initiatives and external partnerships, we'll activate programs to advance Bitcoin education, accessibility and R&D. Stay tuned @MARAFoundation_ to keep up with our work big thanks to Fred Thiel for supporting this initiative 💫🙏

Let’s end this crypto war. ⚔️ 972 days. One developer. Still fighting. 💪 The government didn’t go after bad actors. They went after open-source code. They went after builders. 🏗️ Motions filed. Trial held. Amicus briefs submitted. Thousands of donors showed up. 📜🙌 That’s what this community does — it doesn’t quit. The Fifth Circuit ruled. Congress is moving. The fight is working. ⚖️✊ Free Roman Storm. 🔓 End regulation by prosecution. 🚫 End the crypto war. ⚔️

Code is Speech. New report: coincenter.org/software-is-s…

All Internet policy is about to look like AML/KYC debates in crypto. A big part of the correct answer will be the First Amendment and SEC vs Lowe.

Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now. The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete. The key insight most people miss: for every $1 spent on software, ~$6 is spent on services. The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins. Not "AI for accountants." The AI accounting firm. Not "AI for lawyers." The AI law firm. The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure. That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.

I love my wife. md is how all documents should be stored.

Today, @USTreasury OCCIP announced a new initiative to strengthen cybersecurity across the digital asset industry. Eligible U.S. digital asset firms and industry organizations that meet Treasury’s criteria will be able to receive, at no cost, the same actionable cybersecurity information Treasury regularly shares with traditional U.S. financial institutions. Interested firms should contact OCCIP at OCCIP-Coord@treasury.gov

Just one week to go until Vex Private Markets Summit! Listen to talks by our featured speakers and help us brainstorm how to fix private markets! vexsummit.com @allisondman @AdamDraper @bramcohen @robinhanson @geoburke Josh Klayman @charlesmyim and @JohnCrossmanG

Adam Draper (@AdamDraper) is speaking at the Vex Private Markets Summit on April 11 in San Francisco. His first company was Xpert Financial, one of the earliest secondary markets for private securities. He'll talk about what he learned building it and where private stock trading goes from here. Tickets: vexsummit.com

Claude knows! —> The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite. I. What the Lump of Labor Fallacy Actually Is The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie. The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization. Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself. The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level. II. The Classical Economic Mechanism That Destroys the Fallacy To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously: Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified) When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either: ∙Lower prices for consumers (real purchasing power rises), or ∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or ∙Both. Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs. This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.

AI employment doomerism is rooted in the socialist fallacy of lump of labor. It is wrong now for the same reason it’s always been wrong. More people really should try to learn about this. The AI will teach you about it if you ask! (Hinton is a socialist. youtube.com/shorts/R-b8RR60a…)

Hugely disappointing result. If the Blanche Memo was actually a panacea for developers’ right to create neutral code freely, @rstormsf wouldn’t still be fighting for his freedom. Whether through market structure or elsewhere, developer protections MUST be codified into law.

Unfortunately...

CEXes: not your keys, not your coins RWAs: your keys, not your coins

Nice! Y'all should come to vexsummit.com