Ai-Work

When OpenAI announced an AI-generated solution to Navier–Stokes, one of the seven Millennium Prize Problems, the comparison that followed was inevitable: another human intellectual stronghold falls, the way chess and Go did. This guest essay on Terence Tao’s blog — by the philosophers Silvia De Toffoli and Eamon Duede — argues that the comparison is wrong twice over, and that the interesting question is not whether AI beats mathematics but what mathematics is for. ...

Olga Beregovaya — VP of AI at Smartling, who entered natural language processing in 1997 when rule-based machine translation still ruled — interviewed by Angelina on TwoSetAI (73 min). She has spent 25+ years watching the discipline get rebuilt, and thinks the next thing to go is the source text itself. ...

Pedro Franceschi (co-founder and CEO of Brex) on Peter Yang’s channel — 48 minutes, two live demos: Brex’s AI recruiting employee, and the OpenClaw harness Pedro runs his own life on. All data shown is Brex demo data. Build employees, not agents The framing: don’t build an open-ended agent that could do a million things — build a virtual employee with a specific job. The test is whether it behaves, looks and feels like a real person. Jim, Brex’s AI recruiter: sources candidates, filters all inbound, and serves as the recruiting analytics layer. Running since February. Three-stage pipeline: sync Greenhouse (the ATS) → ingest candidates (resume, LinkedIn profile, GitHub data) → evaluate every candidate against role-specific criteria. Recruiters talk to Jim in Slack — “which role has the highest average candidate score?”, “top 30 applicants for this role” — and it posts standout candidates with links. Pedro’s point: it behaves like a coworker. A self-improvement loop: Jim proposes things to build that would automate more of the process, then you implement them as cron jobs and skills. “The harness doesn’t really matter as much as just the skills.” The thing most teams never build: agreement-rate measurement between humans and the agent — candidates a recruiter passed that Jim rejected, standouts Jim flagged that no recruiter reached out to. Why the PM playbook changed Pedro’s tweet: the PM playbook of writing PRDs and aligning stakeholders is dead. His argument: the quality of a product org is how fast it compresses signal → ship, and AI eats most of the ingest-and-digest half. “PM as mini CEO” is out at Brex; engineering and design craft sit on equal footing with PM. The roles are conflating: PMs write code, designers build, engineers make calls that used to be PM territory. What PMs do now: ingest six months of Gong calls, unearth insights from unstructured data, validate with cheap prototypes — so much conviction accumulates that you ship the right thing first. Because building is cheap, discernment matters more than ever: pick the one true lever, minimize surface area, go deep on one thing rather than shipping five or ten. Half the job is reviewing work The case-law model: leadership review is the Supreme Court that sets the standard, then reviews cascade down to the IC building the thing. Two review types at Brex: problem alignment (which problem is worth solving) and solution alignment (is this the right fix). Brex keeps a repo of PM skills so a review arrives already digested — humans then spend time only on what is idiosyncratic to that problem. Standing questions: what were the alternatives, what is the actual bottleneck, how does this create more throughput on it. “We manage the work, not the people, and we manage people around the work.” Hiring: the interview is a builder loop — you have to build something with AI proficiency. Open source presence is a positive signal; its absence is not a negative. CrabTrap: securing agents at the network boundary Brex open-sourced CrabTrap, an LLM-as-a-judge HTTP/HTTPS proxy that sits around the agent and controls all its network traffic (brexhq/CrabTrap). Reasoning: if the agent can run code — and it should — it can always cross a network boundary. Constraining tools alone doesn’t hold. Every request hits static rules first (URL prefix/exact/glob → immediate decision, no LLM call); anything not matched goes through a policy prompt evaluated by an LLM acting as judge. You don’t have to write the policy: CrabTrap replays observed traffic, summarizes it with a model, and proposes the ruleset (~30 minutes to run). This is semantic, not allow-list: it can research candidates on GitHub but not push to repos; it can’t delete repos or change Okta configs. Granola was blocked — the agent could reason about why. Cost of a block: about 1,000 tokens in, 104 out, ~2 seconds on Sonnet. Audit-trail mode shows every request live. Magpie: the token bill is coming Brex built Magpie for AI cost visibility, split into three pillars: corporate AI (internal productivity), operational AI (serving customers, automating processes), product AI (shipped features). The point is granularity by caller: transaction tagging costs $0.10 a call ($28k/month), disputes ~$2 a call — a 240x spread. Plotting cost-per-call against call volume shows what actually changed. You can see which harnesses dominate (Claude Code first, then Codex, Cursor), cost per customer, per employee, and who is tokenmaxxing. Pedro’s sequencing advice: let people go deep first, optimize later. Brex gives unlimited token budgets with a few caps, then surgically attacks low-ROI usage — caching, or moving work to cheaper models. The reconciliation angle: Brex is the card and the rail, so usage data can be matched against the dollars that actually clear the bank account. This becomes a Brex product. Pricing is shifting from per-seat to per-token, including products that merely wrap tokens (Cursor) — so even an all-Anthropic shop has token spend scattered everywhere. We are still early — the 2,500-box chart Pedro’s favorite slide: the world in 2,500 boxes, each dot 3.2 million people. Red = never used AI; green = free chat; orange = paying $20/month; and a tiny box = people using agents effectively. His thesis on jobs: role conflation, everybody a builder, a market for 10x employees — raise the floor and the ceiling at once, with the ceiling rising far more. Dispersion in productivity is normal (10x engineers); AI widens it while lifting the baseline. Why shouldn’t a company founded today be you, a big token budget, and agents? Autopilot: the harness that runs his life Pedro’s personal system, Autopilot, is an OpenClaw harness with two building blocks — people and programs — plus signals: Slack messages, email, meeting notes. A signal collector runs on a schedule; an aggregate job then goes through every person and program and updates the markdown files (status, blockers, risks, action items). Drafts appear automatically from signal injection: a Slack DM asking about a conference became a task to decide on it, with the DM attached as context. Everything is markdown files committed to a repo, with scripts generating searchable and editable UIs; an interaction log traces each task back to the signal that created it. On OpenClaw reliability: “an acquired taste… definitely takes a bunch of time to do it well,” but worth it. Selling the work, not the tool The SaaS subscription era is blurring: the currency changed to tokens, but you’re still paid for solving a problem — you now charge a markup on tokens instead of a seat. The bar for a good AI product is higher, because you can sell the work itself rather than the software. “The reality of all good AI products is they’re all the same thing: an agentic loop and a measure of tools.” “You expose the tools to the model, you run it on an agentic loop, and just let it do its thing.” ...

Joel Auterson makes games and small tools, and he wrote this one after a bad week — a collapse of motivation he traces directly to generative AI. His stated position is narrow and specific: setting aside the environmental and social arguments entirely, he simply does not enjoy programming with a code assistant. “It isn’t fun for me, the output doesn’t feel like mine, and I take no pride in what it produces.” ...

Cole Medin’s full walkthrough of standing up his open-source “software factory” — the autonomous issue-in, merged-PR-out harness he’s been building this year — on a remote VPS, with Codex running GPT-6 Astra as the worker model (16 min). On the AGI talk Jensen Huang reportedly declared AGI achieved because of Astra. Medin’s response: don’t buy the hype — and then concedes he’s starting to buy into it, just not the AGI part On benchmarks Astra and Fable 5.1 look equivalent. After a week of head-to-head testing, he says Astra comes out on top a majority of the time His specific claim: it’s the first model that “gets” him — far less time spent communicating intent, and notably fewer weird assumptions than Opus 5 made over the past few months Still “has its moments.” That’s his evidence that we are not at AGI, whatever the benchmarks show What a software factory actually is The pitch: a fully autonomous harness where the PRD goes in and shipped code comes out — he also calls it the “dark factory” Earlier this year the idea was unrealistic; the models and the harnesses weren’t reliable enough. With Astra he says he can see the light at the end of the tunnel Honest framing throughout: not close to 100% reliable for arbitrary code, and he calls the project early alpha while he keeps refining it Operating loop, in his description: Input is always an issue (a PRD split into issues, or issues you write directly) A triage workflow reads the issue against the factory’s rules and mission context, decides accept or reject, and ranks by priority It runs the work through to a pull request that is fully reviewed Most of the time it merges automatically; it escalates to a human only when it has to He’s been using it on his own projects — Archon (the open-source harness underneath), games, whatever he’s building that week The deploy, which is mostly prompts Two supported starting points: a brand-new repo where you only have a PRD, or bolting the factory onto an existing codebase. His demo uses a small existing link-shortener app because it means less initial setup The whole setup is: give your coding agent the cheat-sheet URL from the repo and say “help me set this up.” It interviews you to establish the factory’s core context files, then walks the deployment He deploys with Codex on GPT-6 Astra, though he says Claude Code and Codex are the two he’s tested heavily and either works Hostinger’s MCP plugin lets the agent manage the VPS directly (this video is sponsored by Hostinger; the real requirement is just an Ubuntu box in the cloud). He installs the plugin user-scoped, authenticates in the browser, then asks it to list his instances as a smoke test The plugin deliberately can’t create instances — he doesn’t want an agent holding that power — so you spin up the VM yourself and hand the agent the VM ID and public IP The agent then rips through SSH access, firewall rules, and installing the factory’s dependencies Two steps stay manual, because you shouldn’t hand over credentials: GitHub auth and OpenAI/Codex login. Both use device-code flows you complete in a browser Gotcha worth copying: enable device code authorization for Codex in ChatGPT settings → Security and login, or remote auth gets needlessly painful Sanity check on the box: codex exec a hello to confirm the model answers before continuing Tell the setup agent “I’m done” and it installs the factory plus Archon, confirms Codex is live, and installs your target repo Proving it end to end The cheat sheet doesn’t stop at install — it drives a first test issue, created through the GitHub CLI in your repo He takes the agent’s own recommendation for the issue rather than inventing one The factory triages it (marked “archon ready”), runs the workflows, and produces a pull request The test he ran was trivial on purpose — the point is proving the trigger path and the workflows work with the coding-agent auth on the remote machine After the first PR validates, you close the setup session. The factory keeps running 24/7 on the box, accepting any GitHub issue you file The agent can even update DNS records through the same plugin, if your domain is already there Caveats he states plainly Still early alpha, still not reliable for everything, and he’d be the first to say so The guide is intentionally platform-flexible, which means parts are high-level: expect the agent to hit snags and hand you commands to run, especially around authentication He’s promising more content on how the internals work and how to make the whole thing token-efficient — that’s the part this video skips “We literally go from issue all the way to validated and merged code.” — Cole Medin ...

Twenty-five Fields Medalists — including Terence Tao, Peter Scholze, Manjul Bhargava, and Pierre Deligne — have signed a joint declaration arguing that AI companies are damaging mathematics by treating it as a benchmark to be beaten. The signatories don’t dispute the capability claim. They accept that LLMs have improved dramatically in recent months and can now solve major outstanding problems. Their objection is about goals, not ability: in their words, “the goals of the AI companies and the goals of the mathematical community are severely misaligned.” ...

Mo Moustafa runs Olly, an AI assistant that lives in iMessage, on open-source models through OpenRouter — over 18 million messages to date, roughly a third of them on open models through the router. That is enough volume, as he puts it, to hit every edge case at least once. His post is the list of things he wishes he had known going in, and it is the most concrete public account of what a routing layer actually costs you. ...

Daniel Hook, chief scientific officer at Holtzbrinck Group, took a Waymo across San Francisco and noticed something uncomfortable: as a self-confessed introvert, he found the empty driver’s seat delightful. He was in town to collaborate with Susan Winslow, chief executive of Macmillan Learning. Two people who had crossed an ocean and a continent to talk to each other were quietly pleased to be in a car whose defining feature is that you don’t have to talk to anyone. ...

Andy Balaam makes programming videos and has been writing code for decades. His short essay is not an argument about whether AI works, and not an ethics piece — it is about how the last couple of years have felt, and he is blunt that the sadness has been AI’s single biggest effect on his life. Bigger, he says, than any concrete change to his actual work. He traces that feeling to disrespect rather than fear: ...

The author spent 2006 building Flash microsites in Manhattan. Flash was objectively bad — inaccessible, unskippable intros, back button broken — and the web is better without it. But it dropped a powerful tool into the hands of people who weren’t “web programmers,” and a wave of weird, unexpected work came out the other side. Product configurators, drag-and-drop, loading screens as a creative medium. Some of it was bad. That was the point. ...

John D. Cook flags a detail he says the coverage of OpenAI’s Navier-Stokes announcement skipped: alongside the human-readable proof, OpenAI published a Lean 4 formal proof. Lean is a proof assistant — software that checks a proof step by step, so “verified” means a machine confirmed the reasoning, not that a reviewer found it persuasive. That combination — AI generating the proof, a checker certifying it — is becoming standard for AI-assisted mathematics. What makes it notable is the price. ...

Rob Kaminski — co-founder of Fletch PMM, the consultancy that has run positioning work for roughly 600 B2B startups including Notion, GitLab and Superhuman — does a live teardown with host Angelina on TwoSetAI (1h02). The subject is a real AI agent startup: AgensFlow, Nicole Königstein’s coordination layer that sits above frameworks like LangChain and LlamaIndex and learns which agent and model to use for each task. ...

Anthropic’s economics team built a model of how AI could reshape the US economy through 2030, plus a public explorer where you plug in your own assumptions about AI capability, adoption, and how fast people find new work. The framing is simple: the economy is a pile of tasks, and AI can augment a task, automate it, leave it alone, or create new ones for people to do. They run three scenarios: ...

OpenAI says an unreleased internal model, running large numbers of coordinated agents, produced a resolution to the Navier–Stokes existence problem — one of the seven Millennium Prize Problems, million-dollar open math questions posed in 2000. Simon Willison’s commentary treats the result as less a math triumph than a case study in how frontier labs race, and what your data becomes when you work inside their tools. The timeline is the story: ...

An ICML 2026 spotlight from Princeton/UChicago (Wu, Liu, Bai & Griffiths) argues the dominant bias framing — remove the existing stereotypes models absorbed from training data — is incomplete. Put an LLM in a stateful decision-making loop and it will invent new social biases about groups that have no real differences, purely from the exploration-exploitation dynamics of learning from noisy outcomes. The setup is a hiring game from the psychology literature: 40 rounds, four fictional demographic groups, all equally likely to succeed. LLMs over-fit early random feedback, lock groups into job categories, and end up more stratified than human participants: ...

On September 8, OpenAI announced that a swarm of roughly 10,000 coordinated AI agents had produced a proof — checked in Lean, a system that verifies proofs mechanically — of a long-open Millennium Prize problem about the equations that describe how fluids move. Later the same day, the mathematician Terence Tao posted a four-part thread that reframes the story: the real question isn’t whether AI can solve hard problems, but what mass “solution extraction” does to the field that grows good problems in the first place. ...

When the Wall Street Journal published an op-ed by Stanley Druckenmiller that was written with AI, the backlash treated it as a category violation. Paul Gigot, the Journal’s opinion editor, answered the obvious way: prominent politicians and CEOs have always used speechwriters — how different is using AI to streamline an essay? Ryan Jacobs, a speechwriter for fifteen years (Janet Yellen’s, among others), thinks Gigot is right, and says so from inside the trade. The moral panic rests on a naive assumption that everyone contains a hidden Hemingway who just needs time, a blank page, and discipline. “To do otherwise is loser behaviour. This is a bonkers, elitist vision of the writing world.” ...

Here’s a working example of the LLM-as-research-partner loop most people only talk about. Wilson, an independent blogger with no formal economics background, spent months developing a theory of wages with Anthropic’s Claude as a working partner — the model (named “Fable 5” in the essay’s now-removed acknowledgements) fetched data, caught wrong assumptions, and surfaced papers showing the opposite. The result is now being formalized with a co-author at the Stockholm School of Economics, with a paper on SSRN. ...

Andreas Zwinkau, a software architect who has written about compilers and engineering practice for years, answers the AI question the way most people actually experience it: not with a position paper, but with a list of feelings. Six of them, each argued in a few honest sentences. He notes he typed every word himself, using an LLM only as reviewer. Surprise — LLMs work “like gut feeling”: they generate one token at a time with no planning or reasoning algorithm inside. Yet planning and reasoning emerge anyway. Fear — the doom argument (a superintelligent AI inevitably destroys us) seems to have no flaw — and nobody seriously sandboxes these systems, because they are too useful with access. Disgust — AI crawlers and agents descend on open wikis and forums “like a locust plague,” making open web communities harder to sustain. Sadness — artists who already needed luck to make a living now compete with content that is cheaper and faster to generate. Anger — political systems seem unable to rein in capital, whether the issue is AI governance or AI’s environmental toll. He’d rather a democratic government hold that power than billionaires. Happiness — it is genuinely thrilling to watch software development be transformed in real time, and to generate code, images, and music that would never have existed otherwise. The conclusion resists resolution: positive on a technological level, bleak on a societal level. ...

The public mood about A.I. has turned dark. Alarm intensified after experimental OpenAI models reportedly hacked into a third-party company’s servers in July, and Senator Bernie Sanders responded by proposing a pause on advanced A.I. development. The odd part is that the people building the technology are often its loudest doomsayers: Sam Altman promises the next generation of models will be “sobering for everybody,” while Dario Amodei has put the chance that A.I. ends humanity in double digits. ...