Last edition we cheered Claude Tag for finally behaving like a coworker instead of a chatbot. This week is the cold shower: a study says the “coworker” label itself makes teams worse. Around it, Fable 5 got un-banned and came back online, the frontier fight moved into hardware with two custom AI chips in the same week, Anthropic shipped a cheaper agentic Sonnet, and researchers showed how a coding agent can smuggle a backdoor past your review one innocent PR at a time.

Topic of the Week

Last edition, Claude Tag’s whole pitch was that it “behaves like a coworker rather than a chatbot.” A Boston University study says: be careful what you wish for.

Emma Wiles ran an experiment with 1,261 managers in HR and finance. Same AI, same error-filled documents to review — the only thing she changed was the framing. To some it was “an AI tool.” To others it was “Alex-3,” an AI employee. The result: when people thought they were reviewing a colleague’s work rather than a tool’s output, they caught 18% fewer errors and were 44% more likely to kick the questionable stuff up to their manager instead of fixing it themselves. Which quietly deletes the time savings the agent was supposed to deliver in the first place.

The mechanism is the interesting part. Call it a tool and you stay responsible for the output. Call it an employee and something in your head files it under “someone else’s job” — you stop owning it, you stop double-checking. And this isn’t a fringe habit: about a third of managers said their company already frames AI agents as employees, and 23% literally put them on the org chart.

This is why the study matters to us and not just to HR departments. Daron Acemoglu (MIT, 2024 Nobel) puts it bluntly: “AI agents right now are being marketed as things that can replace humans, and I think that’s just a losing proposition. They should instead be optimized so that they can improve human capabilities.” A separate Stanford study of 1,500 workers across 104 jobs lands in the same place — in 47 of those jobs, what people actually wanted wasn’t automation, it was an equal human-AI partnership. Law clerks wanted AI to track case progress; sales reps did not want it deciding customer credit ratings, even though the tech experts thought that task was perfect for it.

For contrast, OpenAI spent the week arguing the opposite direction — that agents are quietly swallowing long-horizon work. Its own numbers: 25.6% of Codex users had made a request estimated to take a human over 8 hours, and non-developer usage multiplied 137x in under a year. Worth knowing, with one asterisk: it’s OpenAI measuring OpenAI’s own product and staff, and the headline metric is token volume — the same vanity number #015’s data already put in its place (commits up 180%, actual shipped releases only 30%). Both things are true at once: agents really are taking on bigger chunks of work, and how you hand that work over decides whether it saves time or just relocates the error-checking to somewhere you’ve stopped looking.

The practical read: the tool-to-teammate shift #018 was excited about is real and useful, and the moment you dress an agent up as staff, oversight quality drops. Keep the human owning the output. The label is free; the 18% is not.

The other half

Two editions ago (#017) our Topic of the Week was the US government yanking Claude Fable 5 and Mythos 5 off the shelf three days after launch, under an export-control directive. This week the arc closes, and it’s the other big one for our crowd: the Department of Commerce lifted those export controls, and Anthropic is bringing Fable 5 back online globally.

It’s not a straight reversal. After what Anthropic calls “a series of productive conversations with the US government,” Fable 5 is being redeployed with a new set of classifiers that block more cybersecurity tasks — and the company warns that, in the near term, some routine coding tasks may get caught in that net too. So if you lean on Fable 5 for dev work, expect it to occasionally refuse things it used to do while the guardrails settle. Mythos 5, Anthropic’s strongest cybersecurity model, isn’t coming back for everyone — only a vetted set of US organizations that “operate and defend critical infrastructure.”

Practical bit: paid plans get promotional access to Fable 5 through July 7 (up to 50% of your weekly usage limit), after which it moves to pay-per-use — and it’s now available inside Claude Tag too. The bigger takeaway is the one from #017, confirmed from the other side: access to the strongest models is negotiated, revocable, and shaped by governments. The model came back, but on different terms.

New Models

Claude Sonnet 5 — near-Opus agentic muscle at a mid-tier price. Anthropic’s headline is “our most agentic Sonnet yet,” and for once the number backs it: 63.2% on SWE-bench Pro, up from Sonnet 4.6’s 58.1% and within ~6 points of Opus 4.8’s 69.2%. The point isn’t the benchmark, it’s the price — $2 / $10 per million tokens (introductory, through Aug 31; then $3 / $15). So the model you’d reach for to run an agent autonomously overnight now costs roughly a third of flagship. It’s the default on Free and Pro, ships with a 1M-token context window, and self-verifies its own output without being asked. If you’ve been rationing Opus for agent workloads, this is the week to re-check the math.

GPT-5.6 got an official preview (Sol / Terra / Luna, with Sol topping Terminal-Bench 2.1 at 91.9%), but it’s still a ~20-partner, US-government-vetted rollout — noted, but nobody here can actually use it yet.

Also worth a line: the open-weight wave keeps rolling — Kimi K2.7, MiniMax M3 and GLM-5.2 are now bundled into third-party coding tools with zero data retention and no training on your code. The open-vs-closed story has quietly shifted from “cheaper” to “you keep control of your data.”

Claude Code & Coding AI

SWE-Together — the benchmark that asks if you can stand working with the agent. SWE-bench asks whether an agent can code when handed the full spec up front. SWE-Together asks what real coding help actually is: a conversation. It rebuilt 109 tasks from 11,260 recorded real user–agent sessions across 36 repos, then scores two things instead of one — did the code end up correct, and how many times did you have to step in and correct it. That second number (call it the “how annoying was this” score) is the one that maps to real developer friction. Finding: stronger agents both finish more and need fewer interventions. If you’re picking a coding assistant for the team, this is a better signal than a leaderboard pass rate.

Paper of the Week

Distributed Attacks in Persistent-State AI Control — the backdoor that arrives one clean PR at a time. When a coding agent ships work across many pull requests against a codebase that persists between sessions, a compromised or prompt-injected agent doesn’t need one obviously-malicious commit. It can smear a payload across a dozen innocent-looking PRs and assemble it later. The researchers built a benchmark for exactly this and found gradual attacks slipped past standard per-diff review 93% of the time — and it generalized across Sonnet 4.5, Gemini 3.1 Pro and Kimi K2.5, so it’s not a one-model quirk. The fix that worked: a “stateful” monitor that tracks suspicious patterns across PRs. An ensemble of four cut evasion from 93% down to 47%. Translation for anyone reviewing agent-written code: reviewing single diffs is no longer enough.

Tool of the Week

Mistral OCR 4 — reads your documents and maps them. Most OCR hands you clean text and calls it done. OCR 4 hands you structure: bounding boxes, typed blocks (title / table / equation / signature) and a confidence score per element, across 170 languages — which is what makes it actually usable inside a RAG or agent pipeline, because now the output is citation-ready. It won a blind human-preference eval 72% of the time against every other document-AI system tested. The part our crowd will care about: it runs in a single self-hosted container, so regulated documents never leave your environment. About $2–4 per 1,000 pages. (Technically launched June 23, just before our window, but it never made a prior issue and it’s too relevant to skip.)

On the Horizon

Custom AI silicon went from slideware to shipping — twice in one week. Two data points that the chip fight is going transformer-native. Etched came out of stealth with working first-pass silicon (an “A0 tapeout” — the very first chip revision came back working, which almost never happens), $800M raised and $1B+ in booked contracts, racks shipping this summer; its Sohu chip is fixed-function, transformer-only silicon. The same week, OpenAI and Broadcom unveiled “Jalapeño,” OpenAI’s first in-house inference chip, taken from design to tape-out in about nine months with “performance per watt substantially better than current state of the art,” first deployment targeted for end of 2026. Inference cost and Nvidia dependence are the two big ceilings on scaling enterprise AI — this is the week the industry started building its way around both.

AI at Tenvalleys

Two days, a dozen-odd teachers, and one question that just stopped being theoretical: how do you teach programming in a world where the AI writes the code?

This week we hosted IT teachers from Zespół Szkół Licealnych i Technicznych nr 1 in our office for a two-day training, part of our partnership with the school, prepping them for a new curriculum that starts in September. The starting point is deliberately uncomfortable: if AI produces working code in seconds, the code itself stops being proof that a student learned anything. Grading has to move from “does it run” to “do they understand it and can they explain it.”

So we built the program around three pillars:

• Digital Campus — one repository per student for their entire time at school, with the commit history serving as a journal of how their learning actually progressed.
• A real-work workflow — Git, GitHub, working alongside AI, and code review.
• Process-based assessment — defending the project and talking through the code, instead of just handing in a file.

The most valuable part wasn’t the tooling, it was the discussion. The teachers pushed on the hard stuff: plagiarism, blind code generation, how to grade fairly when the machine can do the assignment. That’s the tell that we’re working on a real problem, not a hypothetical one — and it’s only the beginning of what we’re building with the school.

Worth noting how neatly this rhymes with this week’s Topic: the answer to “the AI did the work” is the same in a classroom as in an org chart — keep the human owning, understanding, and explaining the output.

For Dessert

While OpenAI’s official account spent the week hyping frontier models and its new inference chip, it also quietly open-sourced Plant Talk — a free project whose entire purpose is to let your houseplant talk to you. Point a webcam at it and the model reads its health off the leaves (spotting the suspicious blotches you’d miss); hold a live voice conversation with it; add a $10 Arduino and your fern can actually tell you it’s thirsty. The build guide is itself the demo — paste the repo into Codex and it walks you through the whole thing. As one developer put it: “Talking to your plants isn’t weird anymore. You can just codex things.”

See you next week,

Jan