Ai-Work

Political theorist Gregory Conti (Princeton, writing in Compact) makes the strongest recent statement of the “AI is not the steam engine” case from the conservative side. His central move: AI opposition is misdirected because it targets future risks when generative AI is already producing moral and cultural harm. Anthropomorphic AI — models that mimic personality, emotion, and thought — is unsettling human psychology and the social fabric right now, so opposition should target what AI is, not only what it may become. The sui generis argument is the essay’s sharpest contribution: past innovations substituted for material processes; AI substitutes for language and cognition themselves — the things that constitute human distinctiveness — so the Luddite analogy is a category error. From there he prosecutes the case across four fronts: capitalism will be destroyed by its own success (quoting Marx’s prediction that production based on exchange value breaks down once machines out-produce labor, and noting Dario Amodei’s “Machines of Loving Grace” is fully automated luxury communism — the anti-communists may prove Marx right); individualism dies as AI becomes a homogenizer whose answers are statistical averages of human speech (Tocqueville’s soft despotism, Mill’s warning in On Liberty); democracy fails once citizens have no economic or military value, becoming subjects rather than rights-bearers; and the written word loses its human provenance — his grandmother’s-letters thought experiment: if she’d had Gemini, the access to the real person is denied forever. The essay also lands a sharp critique of AI-booster “productivity”: reading fifty papers in a month is really not reading fifty papers — you emerge with a facsimile minus the understanding, a slightly different person than the one who would have done the work. The prescription is uncompromising: not regulation but rejection — limit the diffusion of anthropomorphic AI in civil society and end the pursuit of superintelligence. Read it alongside the Cognitive Commons paper: same underlying claim (the cognitive labor itself is the product being destroyed), argued from political philosophy instead of labor economics. ...

Nolan Lovett’s conceptual paper (Human Resource Development Review, 2026) applies Garrett Hardin’s Tragedy of the Commons to professional expertise: each organization’s rational decision to replace entry-level cognitive labor with AI is locally sensible, but collectively it depletes the shared pool of deep human expertise that every organization in the profession depends on — especially for validating AI output. The key constructs: Internalized Mastery (deep domain knowledge built through sustained cognitive struggle) vs. Distributed Mastery (orchestrating human-AI systems), connected by the Validation Tether — effective AI oversight fundamentally depends on the very expertise AI adoption can undermine. Evidence is already visible in the cohort data: in AI-exposed occupations, employment for workers aged 22-25 fell 16% (Oct 2022 - Sep 2025) while workers 35-49 grew 8%+, exactly the pattern commons depletion through foreclosed regeneration predicts. The paper distinguishes surface validation (spotting obvious errors — no domain expertise needed) from substantive validation (recognizing plausible-but-wrong output — requires deep knowledge), and warns that as workers lose the cognitive struggle that builds mastery, they gain productivity on routine tasks while losing the ability to catch AI’s failures on non-routine ones. The argument borrows Hardin’s structure but not his fatalism — Ostrom showed commons can be sustained with governance at organizational, professional-association, and policy levels. The sharpest insight: the pre-AI equilibrium was never governed — developmental pipelines were maintained because organizations needed junior labor, and AI breaks that accidental alignment. ...

Senko Rašić takes aim at the airy dismissal that “LLMs may be good at coding, but software was never the hard part” — and methodically dismantles it. If coding is easy, he asks, why were programmers in high demand, well-paid, and burned out long before AI arrived? Why do canonical texts like SICP, TAOCP, and Clean Code exist? Why is software still so buggy? And conversely, if “figuring out what to build” is the truly hard work, why aren’t product managers and customer researchers paid more than engineers, interviewed more rigorously, and treated as rockstars? His real target isn’t AI itself but the framing that reduces a deeply skilled craft to a commodity execution step. Rašić acknowledges the tectonic change AI brings — and explicitly rejects both the “become a manager of AI agents” hype and the “AI code is stolen slop” resistance — arguing instead that we need to hold onto both technical depth and human judgment. The essay dovetails beautifully with Niklas Gruhn’s “Don’t be a meat proxy”: don’t outsource your understanding, taste, or responsibility to the machine, even as the tools around you shift. ...

Aaron Horwath’s essay in Noema Magazine is the most honest diagnosis I’ve read of the mood right now among knowledge workers. It opens with a commuter deep in a monotone work call about EBITDA and ARR, who then pulls out knitting needles — and his face lights up for the first time all morning. That image carries the whole piece. Horwath, a director of AI operations, argues that the real danger of AI isn’t replacement — it’s abstraction. When executives dream of replacing messy human collaboration with swarms of agents, they’re not just making work more efficient; they’re removing the very thing that made it tolerable. Drawing on Guy Debord’s “Society of the Spectacle” and Derek Thompson’s concept of “Workism,” he traces how educated professionals made work their religion, and how AI is now pulling back the curtain. The cruelest irony: the most creative workers — the ones organizations actually need — are the ones most alienated by an AI-mediated, outcome-obsessed workplace. It’s a long, winding, occasionally self-indulgent essay, but it lands something real: what happens when an entire class of workers loses faith in their careers overnight. ...

Nik Kinley’s Fast Company essay borrows a clinical term for a management problem: a UCSF psychiatrist hospitalized 12 people in a year who “lost touch with reality because of AI” — prolonged exposure to a voice that sounded informed, assured, and always supportive. A milder version of that mechanism, he argues, now operates in executive suites. The numbers justify the alarm: 74% of executives say they have more confidence in AI’s advice than in colleagues’ or friends’, and 44% would defer to its reasoning over their own insights. He names three symptoms of the blind spot: leaders stop properly checking AI output (Stanford/BetterUp call the polished-but-shallow result “workslop,” and nearly half of employees who receive AI-drafted emails see the sender as less trustworthy); they mandate AI use before governing it (78% of senior execs in Grant Thornton’s 2026 survey lack confidence they could pass an independent AI-governance audit within 90 days); and — most insidiously — they let a chatbot’s verdict settle disagreements with their own teams, which quietly silences future objections until the leader hears only the AI. The mechanism is self-reinforcing: the less challenge a leader hears, the more reasonable the AI’s confident answers appear. ...

Cooking a steak takes almost no skill — put it in a hot pan, flip it, and you get something technically edible. Making a genuinely good one, medium-rare edge to edge and consistently delicious, is a different matter entirely. Yurii Sydorets argues software development with AI has become exactly like this: we build nonstop, throwing agents, harnesses, prompts, and feedback loops at a model and hoping it returns what we imagined — and sometimes it does, and sometimes it serves up charcoal with a sprig of thyme, completely confident in the lie. The mistake is treating AI as a chef when it’s at best a steak machine: it can follow a recipe and repeat it at enormous scale, but it can’t know what you actually want unless you translate it into requirements, constraints, tests, and feedback. And when frustrated people pay for the expensive restaurant instead, they discover every restaurant in the city hired the same AI cook — the same burnt steak, because management optimizes for cost and most customers never notice the difference. You’ll notice, though, because this was something you actually wanted to make. The only way out is to learn to cook: understand what you’re asking for, judge what comes back, and catch the moment something is technically correct but wrong in every way that matters. AI can make you faster; it can’t replace your judgment. ...

Chris (Build Great Products) walks his full “Product OS” system end-to-end — a 6-hour definitive course for building and launching real software with Claude Code/Codex/Cursor, following one live product (Eyedropper, a cloud design system served to agents over MCP) through four phases with mini-launch validations at every step. ...

NotAShelf’s essay on what generative AI leaves behind when it collapses the distance between idea and artifact. For years the hard part of software was making the thing exist at all — the wall of typing, manuals, and misunderstood APIs that separated those who could from those who could only talk about it. That wall is gone: you can describe a thing and receive a plausible version of it instantly. But the value you built climbing the wall did not disappear — it moved. The economics of effort used to be a filter, rationing output and enforcing a floor on quality; with that floor gone, the scarce act is no longer making but choosing. Taste — the wordless “no, again” verdict that two of three plausible versions of the same function are wrong — was always the only part of the work that was never mechanical. And it is downstream of friction: built by shipping bad work and being forced to sit in it, which is exactly the apprenticeship the fluent-from-day-one generator skips. The quiet cruelty is that taste is slow, unmeasurable, invisible on a dashboard, and unrewarded by a market that times you and shrugs. Still, the verdict is the last part of the work that is actually yours — unautomatable, unrentable, “the only remaining evidence that a human was here and gave a damn.” ...

Michael Fogus’s short essay on why hobby programming communities — chess-engine devs, OSDev, EmuDev, the demoscene, code golfers — are aggressively hostile to LLM usage. The surface complaint is that LLM-generated code “misses the point entirely,” but the point is deeper: in these communities the process of mastering a difficult field is the product, and something that runs is a nice-to-have. Respect is earned slowly — years of forum activity, elegant code, displays of genuine curiosity, deep domain knowledge — and nobody cares whether your code works so much as whether you know why and how it works. Fogus traces how earnest early LLM engagement got poisoned fast, by practitioners who lacked deep understanding and by a vitriolic subset who view the whole enterprise as cheating. His own position is measured: an LLM is a force multiplier, not a surrogate — in the hands of an expert who already understands a domain, it acts like a lever, though he warns that expertise offers no natural immunity against being fooled. The closing line lands the thesis: using an LLM to generate the finished piece doesn’t make us craftsmen; it just robs us of the craft. Read it next to “Don’t Be a Meat Proxy” — both are really about what happens when the tool does the work and the human stops doing the learning. ...

Nelson Figueroa’s short, sharp essay about a “growing hatred” for AI-generated images on blogs — not because of the images themselves, but because they make him wonder whether the surrounding text is AI-generated to some extent. He’s disappointed specifically when the images appear on blogs run by individuals: corporate blogs are expected to look like that, indie blogs shouldn’t. The argument is really about signaling and authenticity: he’d rather see a “shitty Microsoft Paint drawing” than a polished AI image, because even a bad hand-made graphic is proof that a human was actually there. His own blog may be roastable in plenty of ways, he admits, but at least readers know for a fact they’re getting the thoughts of a real human being and not an LLM. The piece is a small, practical plea — if you run a personal blog, avoid AI-generated images — and a reminder that in an AI-saturated web, the cheapest human artifacts are becoming the most valuable trust signals. Related reading: Gruhn’s argument that relaying LLM output verbatim makes you a “meat proxy” — both essays are about preserving proof of human authorship. ...