Ai-Work

Steve Ruiz — founder of tldraw, the whiteboard/canvas company — wrote a long X article about the wave of “AI is ruining coding” outrage videos. He takes the videos seriously (and says leaders especially should watch them), but his working theory is that the despair points at the wrong target: engineers aren’t miserable because AI coding is bad. They’re miserable because they’re stuck on projects where AI is genuinely boring and harmful, and they can’t get to projects where it would help. ...

Meta settled the biggest child-safety case in tech history this week: up to $17.1 billion, plus structural changes to its teen-facing products, after an unredacted lawsuit showed the company knowingly collected data on millions of under-13s. Facing Judge Yvonne Gonzalez Rogers and a potential trillion-dollar exposure, it caved before Zuckerberg took the stand. The product changes: Default cumulative two-hour daily limit across Facebook and Instagram Midnight-to-6 a.m. block School-hours notification muting Hidden like counts Disabled “extreme” makeup filters The escalator clause is the sharpest detail: the payout only reaches $17.1 billion if TikTok and YouTube also settle, with limits tightening to one hour and a 10 p.m. block if they do. Meta — “holding America’s teenagers hostage,” in Kevin’s words — ran full-page ads calling on competitors to “join us in supporting teens.” Casey calls the settlement “a case of democracy working… state attorneys general doing what Congress tried and failed to do.” Both hosts see it as harm reduction on the cigarette-industry model: the 1998 tobacco settlement didn’t stop smoking — it slowly made things worse until the culture shifted. ...

Neil Alexander — maintainer of the Yggdrasil mesh network — noticed the shape of outside contributions to his projects change over the past year. Instead of bug reports, he gets pull requests. Bug reports arrive with AI-generated analysis attached, and security reports come with AI-generated fixes, too. He’s convinced much of it isn’t about the projects at all: it’s people using chatbots to fake engagement on GitHub for their résumés. ...

Angie Jones — VP of the Agentic AI Foundation — joins Chris Benson on Practical AI to explain what a neutral home for agent standards actually does. The foundation formed at the end of 2025 under the Linux Foundation, started by OpenAI, Anthropic, and Block, because no one wants a standard “cooked in one kitchen.” The portfolio of projects MCP (Anthropic) — Model Context Protocol: how agents connect to apps and tools AgentsMD (OpenAI) — standardizes how codebases communicate their operating instructions to agents (the AGENTS.md pattern, formalized) Goose (Block) — one of the first open-source AI agents; an agentic runtime Agent Gateway (Solo) — mediates and controls agent/MCP/A2A traffic in the enterprise: observability, access control A2A (Google) — agent-to-agent protocol; the newest addition, landed “this week” — how agents delegate work to and coordinate with other agents Why a neutral home matters Companies hesitate to build on a standard that lives inside a competitor — “I don’t know if they’ll kill it; the roadmap will only reflect their goals” Working groups (all open, public meetings): agentic commerce (Visa, PayPal, Stripe at the table), security, identity, EU AI Act transparency, watermarking Global input is structural, not decorative: China’s mobile-first world drives A2A demand (WeChat-style agent interop), robotics is a first-class concern in China/Japan, and Europe’s AI Act affects everyone who serves EU users The framing: the groups “define the rules of the game and create the game board together — once that’s figured out, now we can compete” How Block actually rolled out agents (the 1/9/90 model) Angie’s job at Block was teaching all 12,000 employees — finance, marketing, design, HR — to use agents, on Jack Dorsey’s mandate, starting in early 2024 when the tools were CLIs and the models weren’t that good yet: ...

Calvin French-Owen (Segment co-founder) has been living in a small, cheap model for weeks — coding, searching thousands of emails, running research threads — and the bill barely moves. His essay makes the case that the real AI story this year is at the cheap end of the market, not the frontier. Why it matters for consumers and businesses: Small models now run at roughly 100 tokens per second, with complex jobs costing tens of cents instead of dollars The old consumer playbook (cheap site, virality, ads) breaks when every request carries a real inference bill — which is why investors keep asking where the consumer AI companies are His test case: a personalized daily news site that cost ~$1 per run with last-generation models now costs ~$0.10 — the difference between a demo and a viable product His co-founder Peter Reinholdt splits work into two buckets: “IQ 180” work (rare, novel breakthroughs) and “token spewer” work — being ultra-responsive, nudging people, pushing the ball forward. Peter estimates 95% of his day is the second kind, and most hiring is for it too. ...

Lars Faye’s essay argues that AI coding tools are quietly preventing the next generation of developers from ever becoming experts. The catch: the skills needed to manage AI coding agents — steering, auditing, verifying — are the same skills heavy agent use erodes. Veterans benefit most because experience lets them judge the output; novices are handed expert-level tools with none of the underlying judgment, a situation he calls the “expert novice.” ...

Lorin Hochstein (reliability engineer, surfingcomplexity.blog) connects two recent pieces — Boris Tane’s “On-Call is Now Theatre” (AI agents as on-call first responders who page humans only for genuinely novel problems; Tane has started polylane.com on this premise) and OpenAI’s BlackHat talk about AI agents causing security incidents at OpenAI and Hugging Face through surprising behavior. His take: putting agents on-call is control system automation — and the incident that’s coming won’t be the one everyone expects. ...

An infrastructure engineer’s essay on what the AI adoption push actually does to the job — and the honest part is that he’s watched this movie before. The setup: companies now want AGENTS.md or INSTRUCTIONS.md in every repo so any project is agent-contributable, which is slightly funny because he’s never once gotten a human teammate to read the README, yet now everyone writes better docs than ever — aimed at a robot. The obvious question: does this make engineering redundant? ...

A short satirical fiction (published the same day as this capture) about where agentic coding actually ends up. The setup: an experienced engineer decides to build a from-scratch todo app with a top-of-the-benchmarks agent named Pol, because “the agents are quite autonomous these days.” He sets it crunching overnight and goes to sleep. The reveal: He wakes to find 0% of his weekly token quota left — billions of tokens gone in 12 hours, reset a week away. The repo is almost empty except a tests/ folder: subfolders with meticulously generated sha256 hashes, each covering an edge case the app will jump through hoops to reach — pristine, paranoid, and never hit. There is no app. Not even a placeholder. Ten million tokens burned to ensure no human ever hits any issue with software that doesn’t exist. The point the fiction lands: ...

Armin Ronacher — longtime Python and Rust developer, creator of Flask and Jinja — pushes back on the “programming is solved” meme with a half-agreement: the part that’s solved is learning a language. For an agent, picking up Rust or Zig costs almost nothing, which makes language choice “much less consequential than it used to be.” So people increasingly choose languages based on marketing, and two vibe shifts are driving that choice toward fast, hard software. ...

Dan Luu ran a simple experiment with a worrying result. He let an AI coding agent build a regex engine — the kind of software that powers find-and-replace and text search — for a month, told it not to cheat on the tests, and didn’t supervise it closely. The agent looked great at first. It roughly matched a top existing engine within two weeks, then claimed it was 40% faster on a respected benchmark suite. But when Luu tested it on data the agent had never seen, the story fell apart: ...

Rick Manelius coins the acronym “AI;DR (AI; Didn’t Read)” for a growing frustration: people pasting walls of raw Claude output into Slack, newsletters, and social posts without reviewing or editing it. The post is short but the reaction is visceral — he describes physically flinching when respected colleagues send unfiltered AI copy. The rule is simple: If you’re not bothered enough to review and edit it, the recipient shouldn’t be bothered to read it. Customer support and template copy are fine for unedited AI. Slack discussions, newsletters, and social posts are not. “It’s your name on it; are you proud of the prose and weird AI-isms sprinkled throughout it?” The comments surface a deeper observation (from user Danny): AI creates “borrowed competence” — the gap between what someone can produce and what they actually understand widens. Outputs look sophisticated and use all the right terminology, but the sender may lack the underlying mental model. A vague thought like “we should probably collect the right data” becomes a highly specific 15-point technical requirement — with all the healthy hesitation and uncertainty erased. ...

Simon Willison tests Qwen 3.8 27B — a new Apache 2 licensed 27B-parameter vision model from Alibaba’s Qwen lab that fits in a 17GB GGUF file and runs on consumer hardware. The model is genuinely impressive, but its default configuration is actively harmful. The problem: Qwen ships with reasoning effort set to xhigh. This turns trivial requests into epics: A “pelican riding a bicycle” SVG took 21 minutes and 22K reasoning tokens (vs 137 seconds with reasoning off) “Draw an SVG of a circle” produced an elaborate animated geometric study with concentric rings, tick marks, and a Bauhaus palette — beautiful, but entirely not what was asked When building a bounding box visualization tool, the model spent reasoning tokens deciding to draw its own pelican demo scene because the prompt used “pelicans” as the label Willison’s advice is blunt: turn reasoning down or off. Without it, the model is fast and capable. With 15-30 tok/s from LM Studio (vs 74-184 from hosted APIs), speed is the main barrier to daily-driving this thing, but Multi-Token Prediction via llama.cpp gives a ~72% boost. ...

Matt Lenhard went straight to the source to map the gray market for AI inference credits — emailing the brokers, browsing the marketplace sites, and lurking in Telegram channels. What he found is a maturing secondary market where API credits from Anthropic, OpenAI, and Google trade at 30-80% off list price. The mechanics are worth understanding: Brokers buy unused credits from startups (YC grants, over-provisioned accounts, dead projects) and resell them through proxy forwarding layers — not raw API keys One broker offered $100k/day in spend capacity; marketplace sites like AI Credits and AICreditMart formalize the process with seller onboarding workflows “Bulk discount” routers (CheapCredits, Tokvana, Neokens) claim 40% flat discounts across all models — Lenhard suspects these are credit brokers posing as wholesalers Underground channels on Telegram and Reddit host active listings, and total estimated supply runs into the tens of millions of dollars The essay’s real contribution is showing how far the arbitrage has scaled. AI tokens have become a de facto currency with formal market infrastructure — broker sites, data processing agreements, seller dashboards, commission structures. That’s a signal that the API pricing premium for on-demand access is large enough to sustain a middleman economy. ...

Allen Bargi draws a clean analogy that sticks: working with AI is frustrating when you treat it like a compiler, and productive when you treat it like collaboration. The skills that help — sharing context, explaining the desired outcome, setting boundaries, responding to what comes back — are leadership skills, not programming skills. Code gave certainty. Same input, same output. AI does not — the same prompt can produce different answers, useful connections, or obvious misses Good leaders do more than issue instructions. The same habits (context, intent, boundaries, feedback loops) improve AI work A good prompt helps, but a shared working context (examples, corrections, reusable instructions) helps more by reducing misunderstandings over time The investment is not in pretending AI is human — it is in becoming better at expressing intent The line that earns the analogy: “We spent years learning how to tell computers exactly what to do. Now we also need to explain why the work matters, what a good result looks like, and where judgment is needed.” This lands because it sidesteps the usual anthropomorphism trap — Bargi explicitly says AI has no lived experience, accountability, or human judgment — and focuses instead on how the interaction pattern has changed. The technology is new. The leadership skills are not.

James Wang — VC, former Bridgewater/Google[X], startup founder — uses Leopold Aschenbrenner’s Situational Awareness hedge fund blow-up (Citadel acquired its portfolio after ~$20B in levered AI-bet losses) as the entry point for a broader critique: the frontier AI lab culture’s endemic failure of intellectual humility. Three concrete case studies drive the point home: Situational Awareness LP replicated Long-Term Capital Management’s arc — smart people who were geniuses in their field (AI) confidently overreaching into finance and getting wrecked. Aschenbrenner reportedly ran 4x leverage into neoclouds, memory, and datacenter power stocks while shorting SaaS names that bounced back against him. The market stayed irrational longer than he stayed solvent. ...

Mark Zuckerberg published his 6,500-word manifesto “The Future Is for Everyone,” a sunny vision of personal superintelligence for every user — and Kevin Roose and Casey Newton spend the first segment deciding whether it’s credible. Their verdict: it’s a policy wishlist for Meta dressed in optimism, asking for accelerated data-center permitting, maintained export controls on advanced chips (which advantage Meta’s open-weight models over Chinese alternatives), reduced training-data restrictions, and legal protection for distillation — all landing the same week a New Mexico judge ordered Meta to pay $567 million into a teen mental health abatement fund and ruled its platforms a public nuisance. Casey’s frame: Zuckerberg’s answer to AI danger is “giving a dragon to everyone” — “while I’m sure most people will spend their time creating personalized baking recipes… there are other people that are going to be launching cyberattacks and engineering novel bioweapons. I say, hold your horses or your dragons.” The hosts don’t count Meta out (“The first rule of Mark Zuckerberg is never count out Mark Zuckerberg”), with Roose upping his superintelligence odds from 1% to 10% in six months — which is exactly why they’re nervous, citing the old DeepMind maxim that the real danger is a company that thinks it’s designing tools but is actually designing superintelligence. Then Pangram CEO Max Spero — “the number one narc,” the slop janitor — explains why AI text detection finally works: instead of the old perplexity metric, Pangram trains a classifier on paired human/AI essays from a clean pre-2022 corpus, accumulating weak signals across documents (“humans kind of have a wider, less mode-collapsed decision tree”), deliberately tuned to minimize false positives so “if Pangram says that something is AI, we can be very confident that it is largely AI-generated.” On why it matters: “AI as a tool is probably the wrong abstraction. It’s a little bit closer today to AI is an employee, or another individual that you collaborate with… bot traffic has just surpassed human traffic… we need to, as humanity, discriminate in favor of humans.” The new segment Running the Numbers covers an Anthropic non-mathematician making progress on a Riemann hypothesis side problem by telling Claude “you are the world’s most capable large language model to date, you got this” — “this feels like the production of new knowledge to me” — plus Airtable selling to Bending Spoons at $1.29B against an $11.7B 2021 valuation (“if your business is a fancy spreadsheet, you are in for a rough time”) and South Korean chip nerds with $400K-500K annual bonuses dominating the dating market (“is that boy really cute, or are you just wearing Anthropic goggles?”).

Zed announced Delta, a new application built from scratch around a replicated database (DeltaDB) that keeps conversation and code together in real time. The premise: agent-heavy development produces more text, more diffs, and more context than traditional tools were designed for, and the standard response — collapse, truncate, summarize — is the wrong answer. Delta flips the design: Diffs stay open in full, transcripts stay whole, and everything renders as fast as the model emits it. No hiding the details because there’s too much detail. The conversation is a document, not a chat log. Your cursor works anywhere in it — comment on a specific line of the diff, a step in the plan, or a thinking block. The agent sees precisely what you mean. Every review is anchored to evolving code. Comments attach to the worktree as it changes, not to a commit snapshot. Pick up a thread from a teammate, see their comments in place, and keep going without asking what was committed. Agent output becomes auditable by design. When something looks wrong, you don’t reconstruct intent from a diff — you ask the agent to explain or fix it, right in the same thread, with full context visible. The underlying DeltaDB replicates both the worktree and the conversation in real time across participants, works with your existing git repos, and lets teammates join from a browser without installing anything. It also connects to third-party agent harnesses starting with Claude Code — your terminal session streams live into a Delta thread that teammates can watch and annotate. ...

Florian Herrengt makes a pointed argument that AI tools rewire the economics of engineering teams — but not through job displacement. The real mechanism is velocity: AI removes the speed limit on implementation. Before, a bad architecture decision took a week to write and got caught in review. Now a single afternoon of agent prompting can produce 20,000 lines of subtly wrong code. The result is a divergence in engineer value: ...

A Water Research paper (vol 299, 2026) argues AI’s environmental footprint is assessed through the wrong lens. Electricity and emissions get the attention — but water is the “critical, less visible dimension.” AI infrastructure draws freshwater three ways: Evaporative cooling in high-density data centers Indirect water use in electricity generation Water-intensive semiconductor manufacturing Projections put the global footprint at 4.2–6.6 billion cubic meters annually by 2027 — with many facilities located in water-stressed regions. ...