Greg Gunn

Mark Zuckerberg just argued that AI will force companies to hire more people. Not fewer. Three and a half billion people use Meta every day. Not one of them has a phone number to call. Mark Zuckerberg: “It’s clearly just going to automate jobs and like all these jobs are going to go away… that has not really been how the history of technology has worked.” The entire media cycle runs the same story. AI replaces workers. Industries hollow out. The human becomes unnecessary. History has never once cooperated. Voice support for 3.5 billion daily users costs between ten and twenty billion dollars a year. The math made it untouchable. So Meta never built it. AI changed the math. Zuckerberg: “Let’s say the AI can handle 90 percent of that… you’ve gotten the cost of providing that service down to one 10th.” A service that could not exist becomes standard. Overnight. The moment it goes live, the edge cases arrive. The escalations. The problems no model can close alone. Every one needs a human on the other end. Zuckerberg: “I actually think we’re probably going to go hire more customer support people.” The AI did not kill the jobs. It unlocked a service so vast the company now needs people it never would have hired. When execution costs crater, companies do not pocket the savings. They go after problems they could never afford to touch. New markets. New products. New services that were economically impossible twelve months ago. Every one creates roles that did not exist before the machine arrived. The people terrified of automation are tracking the wrong number. They count the jobs that disappear. They have no framework for the ones that haven’t been invented yet.

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.

Demand for recruiters is surging The number of open recruiter roles is almost back to 2022 peak levels. This role got hit the hardest post-Covid, and also recovered the quickest. By definition, recruiting headcount expands and contracts with hiring demand, so it’s likely a leading indication that we’re tracking toward sustained highs in hiring demand in tech.

@eglyman I built an AI Agent that interviews all your employees and maps all the A+ builders they worked with in the past.

We only hire builders (and we’re on a hiring spree)! Reply with something you've built. I'll read them personally. We’re interviewing the best ones. You’ll be a good fit if you: - work best without permission - default to “how could I automate this” - had weird teenage hobbies - spend your sunday making side projects - have more Claude agents than cousins - shipped something this week - make prototypes, not powerpoints - don’t like hierarchy - are good at games: chess, monopoly, poker - would take dinner with Elon over $100k Good luck, Eric

The death of inbound applications is upon us: and yes, it’s in a big part because of AI making it dead simple to apply. And so inbound applications become noisy, with increasingly more of non-qualified people. And so companies rely on referrals and recruiters to source instead.

12 years later the VC who passed on Figma’s seed because Google could kill them is finally feeling seen

high/low light of the week: lowest was at the beginning of the week, when i was asked: agi, big labs take all, what are you gonna do high: interacted with reality of how the world really works (not how the world looks on twitter), and realized a) when big labs get to agi, open source will get to agi 3 months later (thanks to folks around who brought sanity to this: @gunnr, and different private convos) b) agi != ai automatically deployed in companies/life. most of it is about soothing the emotions that hinder ai + human collaboration

Right now, with extreme levels of uncertainty, founder dynamism is the single most important thing I’m evaluating. Can they navigate what none of us can predict?

@Hadley IYE have you noticed a noticeable shift in VCs towards “protected waters” in the last 2 weeks?

Ben Horowitz: Your ONLY job is "right product, right time"

@MaxJunestrand @WeAreLegora Smart move. Lemme know if you want moar :)

Today, we announced our acquisition of Walter AI. They’ve built agent-native systems alongside lawyers that handle real workflows. We share the same blueprint for the future - and I'm excited to welcome the Walter team to @WeAreLegora! legora.com/newsroom/legor…

Same

Every VC is understandably scared of making investments that will get crushed by the model companies. So they’re searching for things that feel protected. I wonder if that’s the right approach. Focusing on downside protection has generally been a terrible way to invest in startups.

@morganlinton @denisyarats same here. we did our research and decided to not go with mcp. our agent arch is api based. cc @JaclynLunger

we’ve been saying it when we explain our agent arch 🥹

@yongfook Would love to intro you into a cracked team in the current a16z Speedrun batch tackling this.

MCP never made sense to me. You're saying we made a machine with the knowledge of all mankind, and now we need a special way to interact with it? Instead of protocols it already knows? Abstractions like MCP are just someone's attempt at gatekeeping or owning an ecosystem.

@levelsio There is a cracked team at a16z Speedrun that is solving this today. Dm if you want intros/early access.

Thank god MCP is dead Just as useless of an idea as LLMs.txt was It's all dumb abstractions that AI doesn't need because AI's are as smart as humans so they can just use what was already there which is APIs

@Austen There is a killer team in the current a16z Speedrun batch tackling this. Will intro.

Who is building the better auth layer for agents? Because what we have currently is miserable

Bret Taylor, former co-CEO of Salesforce and chair of OpenAI, just redefined the unit of productivity. It’s not a person. It’s a process. Taylor: “I think the atomic unit of productivity in AI is a process, not a person.” AI won’t replace a worker. It will compress entire workflows. What used to take 17 days across departments collapses into hours. The traditional corporate model measures productivity in person-hours. The new model measures process-compression. The incumbent assumption: you buy AI to replace a junior analyst. That’s a fundamental misunderstanding. You deploy an autonomous agent to completely collapse the timeline of a business outcome. An operation requiring 17 days of bureaucratic friction gets mathematically condensed into 17 hours. You’re not buying a digital employee. You’re buying the ruthless compression of time. Using AI to speed up a single employee’s task? You’re playing the wrong game. Taylor: “There’s a legal department to do a contract. There’s some finance department, procurement. You probably have IT that’s involved to onboard them into your core systems.” Friction in the modern enterprise doesn’t come from a single worker. It comes from the endless hand-offs between siloed departments. The traditional CEO tries making each department 10% faster. The winning CEO deploys an AI overlay that autonomously bypasses the human hand-offs entirely. The algorithm doesn’t sit in the legal department or IT. It executes the entire thread simultaneously across all core systems. It doesn’t replace individual workers. It renders their departmental bottlenecks completely irrelevant. Taylor: “I think it’s wrong to think about AI as sort of replacing people. In addition to being inhumane, it’s just sort of nonsensical because AI sort of operates in the world of digital technologies.” The neural network won’t sit at a desk, pour coffee, or shake a client’s hand. It’s a sovereign engine operating exclusively in the realm of digital friction. Superintelligence isn’t your direct replacement. It’s your digital exoskeleton. The hard part of enterprise execution has never been the human element. It’s always been wrestling with archaic, fragmented software systems. When AI takes over the digital process, the biological operator gets freed from the bureaucratic drag. They instantly shift from manual processor of forms to high-leverage director of outcomes. And that’s the real transformation. Not humans versus machines. Humans commanding the compressed timelines machines execute. Whoever builds that infrastructure first turns every competitor’s 17-day cycle into a fatal disadvantage. Because they’re finishing in hours what the rest of the market hasn’t even started.