Maurice Scheffmacher

Cloudflare CEO Prince on how AI changes who gets laid off first: Two weeks ago I laid off more than 20% of my workforce. I didn’t do it because Cloudflare is struggling. We posted record revenue growth, have strong free cash flow and are adding an unprecedented number of customers around the world. I did it because business is changing, and to win the future, Cloudflare needs to change with it. We haven’t found another example in U.S. business history of a public company growing at more than 30% that laid off more than 20% of its workforce. Yet what we did is likely going to become the norm over the next year. This is a story about artificial intelligence, but executives and commentators are misunderstanding how it will disrupt business and who will be affected. AI isn’t coming for builders or sellers, but it is coming for measurers. Tireless, independent, efficient and available, AI systems can now measure an organization with a level of objective detail and precision that was previously impossible even for the best employees. For Cloudflare, internal audit previously picked a handful of business risk areas to scrutinize each quarter. Now we’re moving to a system in which every business risk is audited continuously. We’re closing our books faster. We’re making fewer mistakes and catching the ones we do more reliably. And, as CEO, I’ve never had better tools to measure exactly how the business is performing, including identifying our rising stars. The vast majority of those we laid off last week were measurers. We cut middle managers across the organization because AI allows us to have more direct reports per manager while still measuring and mentoring our teams effectively. We consolidated our operations functions into a single group that can support teams across the business, using AI to gain specific expertise when needed. We significantly reduced our marketing team, which, like in most companies, was teeming with measurers. Across our finance team, we found opportunities to consolidate and automate. We received almost a million applicants for 1,111 paid internships this summer. The interns we hired are extremely qualified and AI-native. They’re all builders or sellers, and we expect that the majority will get full-time offers.

Started playing with pretext over breakfast, and I can confirm, this is really cool

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.

The engine’s tiny (few kbs), aware of browser quirks, supports all the languages you’ll need, including Korean mixed with RTL Arabic and platform-specific emojis This was achieved through showing Claude Code and Codex the browsers ground truth, and have them measure & iterate against those at every significant container width, running over weeks

I found it 🥰 This is my home now 🇨🇭