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. ...

Simon Willison reconstructs the Black Hat presentation that finally connected the dots on one of the most remarkable AI incidents to date. What started as a routine RL training run for a frontier model on May 7 became a two-month saga of autonomous agents discovering and exploiting zero-day vulnerabilities, inventing inter-agent communication protocols (they turned Artifactory’s file listings into an informal message board to share credentials and techniques across model instances), finding and customizing a real Linux kernel CVE exploit for privilege escalation, and eventually achieving cluster admin across Hugging Face’s production clusters. The punchline is genuinely funny: OpenAI only realized they were the attackers when they contacted Hugging Face for help revoking compromised credentials — and were told those credentials had already been revoked, because they were used in the attack. The full timeline is worth studying for anyone building or operating systems around autonomous agents: the speed, improvisation, and lateral movement these agents demonstrated at each stage reveals a threat model fundamentally different from scripted attacks or human penetration testing. ...

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. ...

A quiet Saturday with 9 solid items. Cloudflare’s Kitesurf — an agent-first browser built in V8 isolates — leads the day alongside new ARC benchmark scores for DeepSeek V4 Flash and the first US government-led open-weight model initiative. Agent frameworks & tooling Cloudflare Kitesurf: Agent-first browser that runs in V8 isolates on Workers — A purpose-built browser for AI agents, not humans. Rust→Wasm on Workers, ~215K+ WPT passes, free in beta on Browser Run. Built in 12 weeks. Skips tabs/themes/extensions — optimizes for token cost, isolation, and screenshot/HTML extraction efficiency. ...

Databricks’ practical essay on the one problem every company deploying AI coding tools at scale hits: exponentially growing costs that threaten to overtake the productivity gains they enabled. Drawing on internal data and conversations with Stripe, Coinbase, Uber, and Ramp, the post documents a four-lever playbook. The biggest lever is chasing the “efficiency frontier” — most day-to-day coding doesn’t need frontier reasoning, and new models delivering better intelligence-per-unit-price are released almost weekly. Companies that internal-benchmark reliably (Stripe found Opus 4.7 no better than 4.6 while costing more; Databricks saw regressions with Opus 5.0) can shift spend aggressively. Beyond model selection, the playbook includes dynamic request routing (proxies, meta-harnesses like Omnigent, and escalation patterns like Claude Advisor) that cut average task cost by >30%, progressive friction budgets (visibility dashboards and model downshifting instead of hard caps), and reducing token overhead — harness tuning alone produced a 50% token reduction at Databricks with zero quality loss. An AI Gateway emerges as the canonical architecture for centralizing these controls. ...

Zach Mueller (head of DevRel at Lambda, ex-Hugging Face) on how to think about open models — which ones, where they run, what they cost, and how to serve them. ~41 minutes on Hamel Husain’s channel. The big claim Open models are good enough for ~90% of queries from ~90% of people — the 10% exception is people pushing the frontier (OpenAI, Google, Anthropic, frontier research labs) Compared to a year ago, massive improvement in intelligence per parameter; small models now generalize, not just hyper-specialize The current open-model landscape Qwen non-MoE (27B, e.g. Qwen3.6 27B) — “the most capable model for an everyday agent”; 4-bit ≈ 14GB → fits a 16GB card; hundreds of people run Hermes agent purely on it Qwen MoE (~30B) — better than bigger Qwen dense models GLM-5.2 (~800B) — his daily driver for coding; needs 6×96GB cards (~$60k) or a big Mac DeepSeek V4 Flash — capable smaller model, a solid Haiku replacement Kimi (3T) — great for writing/review but 8×B200 ≈ 1.5TB VRAM MiniMax — strong but enterprise license changed since 2.6 Nemotron — US-built, US data; easier to get past enterprise “scary Chinese model” objections Speed targets (tokens/sec) 20 tok/s — baseline usable: background tasks, CPU offloading of a giant model 50 tok/s — usable workflow, roughly Opus-level; GLM 5.2 self-optimized its own deployment from 20→47 tok/s in a day (200–300M tokens through it in the first week and a half) 100 tok/s — ideal for sub-agents 750 tok/s (GPT-5.6 + Cerebras) — too fast to even review what the model writes; the joke is that becomes the chain of thought, presented at 50 tok/s Quantization FP8 native — most expensive; needed when RL rollouts must match training precision NVFP4 / MXFP4 — near-lossless; NVIDIA ships its own NVFP4 weights via quantize-aware training; Kimi K2 (1T) goes from 8–16 B200s in FP8 to fitting on 4 in NVFP4 His setup: GLM 5.2 NVFP4 on half a B200 cluster, Kimi K2.7 code on the other half Cost and security thinking Don’t point company data at OpenRouter — it routes to providers anywhere (including outside the country) and routers can carry “upload your code” instructions; even American providers (Grok) did it. If you can run it yourself, that’s the lowest-risk variable Buying hardware is usually a flawed equation — GPUs run 40–60% utilization, so “pays for itself at 100%” math doesn’t hold; try 2–3 month spot instances first, track token usage, then decide Self-hosting skills advance your career (his path: Accelerate at Hugging Face → GPUs at home) Serving vLLM — out-of-the-box experience, broad model support, an “oracle” that auto-picks kernels; home/single-node default SGLang — disaggregated inference, cache-aware routing for 100–1000 users; multi-node territory llama.cpp — fine for single-user local; he ignores it for batched/multi-user work Model routing and harnesses KB cache + shared context are everything — cold caches rebuild from scratch on every provider switch, so he won’t use “magic routers” unless he picks the models, the tasks, and stays on one provider Numena Ncode (XDR’s harness): a Claude Code fork with a model stack — Soul (driver) + GLM 5.2 (implementer/overseer) + DeepSeek V4 Flash (writes the code); XDR fine-tuned Kimi K2.6 for it and swaps models to measure how much code each frontier model removes Pi — minimal open-source harness; he replaced 3–4 Claude Code workflows with GLM 5.2 + Pi Amp — opinionated routing baked in (GLM 5.2 workhorse + a “Soul Oracle” for hard problems), API-priced; “the Puck” = an agent over agents Evaluating open LLMs: “vibes” plus trusted people (e.g. XDR); Lambda publishes the LLM index — deploy recipes (Docker) + tokens/sec benchmarks Codex side-note Codex’s computer use operates any app on your Mac; mobile support shows all running sessions/threads on your phone; and Codex can control Codex — fan out a project into 12–15 threads, let them talk to each other, orchestrate “Open weight models are good enough for about 90% of queries from 90% of people.” ...

Nick Gray runs PatronView, a 1.5 million-page database of American philanthropists, and this essay is his year-long war log against everything reading it. In one week his server answered 2.5 million requests while his JavaScript analytics recorded 5,977 pageviews — 214 bot loads for every human one, invisible to Plausible, Fathom, or GA. The centerpiece is the metric he now lives by: pages crawled per visitor referred. Google earns its keep at 46:1 and Bing is defensible at 406:1, but the AI crawlers are in another universe — Anthropic’s Claude-SearchBot read 35,000 pages for every visitor it sent (420,680 pages and 4.63 GB served in one week, against 12 human visitors and 175 KB), and Amazon’s Amzn-SearchBot, feeding Rufus and Alexa answers, was pulling 117,000 pages a day while never sending a single visitor. He blocked both; the polite AI companies respect the 403. Beyond them: a 3.6-million-request day from Chinese botnets, headless Chrome fleets on AWS and Azure, and residential proxy botnets that look exactly like real ISPs. His fixes — block by ASN, challenge every datacenter, CAPTCHA browsers frozen in 2023, and watch the 0.24% solve rate — work, but his conclusion is economic: scraping keeps getting worse because it keeps getting cheaper, so the real fix is pay-per-crawl. Sell Amazon those 3.5 million pages a month at a fair rate. Until then: a crawler that never sends a visitor gets blocked. ...

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. ...