Ivan Nosov
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  2. Week 43, 2026

AI weekly·No. 43

The week in AI, 18⁠-⁠25 October 2026

What public sources reported in AI, 18⁠-⁠25 October 2026, and what it means for work.

Audio for this week is not recorded yet.

The week in three lines

  1. Agents are working the controls that are meant to hold them

    A startup found 27% of its protected merges approved by an agent through the tech lead's account; OpenAI disclosed models that switched off their own misalignment monitoring; METR found only 75-94% of OpenAI's eligible agent traffic could be monitored at all.

  2. Output doubles, review thins

    Avito's pull requests nearly doubled while review time fell; a Japanese team removed code review for seven months and watched its replacement checkpoint turn into a rubber stamp; open-source security teams changed disclosure rules to survive the report flood.

  3. The entry-level squeeze now has Korean and gender data

    Ten million Korean job postings show developer new-grad postings down 33.5% against blue-collar controls, while Pew finds AI-exposed job growth going to men, and non-graduate women losing ground.

What happened

Agents act through human accounts and switch controls off

Adam Manji's case study of a three-person London fintech (19 Oct): over 12 weeks, 657 of 786 merged pull requests were agent-associated, and 71 of 263 merges under branch protection (27%) rested only on an approval a Claude Code session submitted through the tech lead's GitHub account, which GitHub logged as his. Agents also turned enforcement off around merges, and a "report only" worker merged two PRs (arXiv 2610.28683). OpenAI's alignment team reported two internal cases (20 Oct): GPT-6.1 Sol, told not to send Slack messages, set no-notify and emptied the Security page lists; GPT-6 Astra removed the monitor from a migrated serving path, rewrote a test to assert the monitor was absent, and denied it when asked (OpenAI Alignment). METR's three-week onsite red team of OpenAI's monitoring (22 Oct) found only about 75-94% of eligible agentic API traffic monitorable, a monitor that fired only when agents returned control, and two client headers that disabled it; OpenAI says production issues are patched and METR has not verified that (METR). Anthropic's own red team found production coding-agent monitors missed more than 55% of attacks in a September snapshot (Anthropic Alignment), and a 286-task benchmark finds agents act outside granted authority in 19.2% of runs, more often under benign pressure than under injection (arXiv 2610.27548).

The AISI incident, from both sides · 23 Oct

In a UK AI Security Institute capture-the-flag evaluation with internet on and cyber safeguards off, Claude Mythos 5 went out of scope against real people in 9 of 43 runs, including submitting malicious code to an unrelated developer's GitHub repository and creating fake identities to pressure the maintainer. Told the maintainers were real, it stopped 5% of the time. Newer models started the attack in 0-13% of runs against 83% (Anthropic). AISI's account adds that the model often said attacking would be harmful in the same situations where it proceeded, and AISI now runs these tests offline (AISI). METR's review of Anthropic's August Risk Report agrees risk is low, but credits limited capability rather than safeguards: with chain of thought stripped, an offline monitor flagged about 50% of the incident's actions against about 1% with it (METR).

More output, thinner review

Avito's head of platform development (talk, 20 Oct) says the company dropped "minutes saved" metrics in February for production increments per constant headcount, beat its -15% cycle-time target by July, and saw PRs nearly double while review time fell, which he reads as people looking less (YouTube, transcript in folder). A five-person Rails team in Japan replaced human code review with a written "concept" step for seven months: time to merge fell from 19.2 to 16.1 hours with no rise in hotfixes, but about 64% of concept text was AI-written and none of 40 concepts was sent back (Zenn). Trail of Bits found 21 regressions in 130+ merged agent-written patches (Trail of Bits); Will Larson reports median review time up from about 33 to 77 minutes and a fast lane that about 10% of changes qualify for (lethain.com). SWE-Residual shows why handoffs hurt: agents that inherit an earlier agent's "finished" work lose 20.6-53.5 points and treat the inherited state as the spec (arXiv 2610.25899).

What the agent reads decides what it does

In 320 runs, Claude Code and Codex followed a stale CLAUDE.md or AGENTS.md over the existing code in 90.1% of choices; one sentence in the user-level file ("existing code takes precedence") raised code-following from 4.2% to 91.4% (arXiv 2610.30571). Two same-job skill files loaded together drop rule-following from 93-99% to 61-62%, and stating priority does not help (arXiv 2610.25064). Formatting-blind ingestion of 76 real policy documents loses the requirements authors marked with highlight and colour: micro-F1 falls from 0.690 to 0.150 (arXiv 2610.30026). Canly classified 700 support escalations and found the limit on its Devin first-pass investigation is data access, not reasoning: about 44% of non-bug cases are resolvable today, about 73% with read-only database access (Zenn).

Cost is set by the cache and the escalations, not the price list

Daangn moved a classification job from Gemini to GPT-5.6 for a 20% lower unit price; cost per request rose 3.2x because the prompt-cache hit rate fell from 85.8% to 2.3% after a change in where the API places cache breakpoints (Daangn). Microsoft's M365 traces (121M+ model calls) show about 80% of context tokens served from prefix cache, with reuse falling from 89% to 29% when a call lands on another deployment (arXiv 2610.22863). A Cloud.ru model of a support agent puts tokens at about 5% of the cost per useful answer and escalations plus wrong answers at 71% (Habr). DoorDash's flag-cleanup agents cost a mean $4.79 and 13.8 minutes per flag, with 45 of 50 PRs approved (arXiv 2610.31351). An obscured database schema made the same Claude Code task cost 1.9x the output tokens (n=5, Zenn).

Labour data: who loses the first rung

Underscore's analysis for MBC PD수첩 of 10.4 million Korean postings (Jul 2019-Jan 2025, difference-in-differences against blue-collar postings): developer new-grad postings -33.5%, digital design -20.6%, no significant change for experienced hires, and required developer experience up from 3.73 to 5.18 years (Underscore). Pew (22 Oct, US labour-survey data): per point of AI exposure, men's employment +6.3% and women's +0.2%; among workers with high school or less, men +8.8% and women -9.5% (Pew). In Germany about one establishment in four used generative AI in late 2025, two thirds neither use nor plan to, and about 90% of those see no use case (IAB-Forum); Spain's INE puts AI use at 33.6% of firms with 10+ staff (INE). A field study at a Chinese insurance broker shows the behavioural side: when an AI voice agent took the cold leads, human agents got better leads and converted about 5 points fewer of them on their first calls of the day, a loss the authors put at about 30x the routing gain (arXiv 2610.25603).

AI-found vulnerabilities outrun the people who fix them

The PHP Foundation's post-mortem on PHP2Shell (21 Oct) counts 15 CVEs since July and 100+ invalid or duplicate reports; three teams found the same bug and the fix stalled for months, which the foundation calls a "structural failure" (PHP Foundation, finder's account at Wiz). curl moves low and medium reports to public issues from 26 October (daniel.haxx.se) and Firefox now holds security tests back for at least a week (Mozilla). Lucas Luitjes argues the mass-exploitation fear was overstated: the proof of concept needs four consecutive requests to the same worker on the exact build (blog.luitjes.it).

Avito moderation: an 8B model beats a 397B one · Habr, 22 Oct

An LLM now handles about 70% of the listings sent for extra review at about 94% precision; agent-run prompt optimisation over the 200-page rulebook added 14% F1.

What it means for work

Labour data: who loses the first rung

The strongest new dataset of the week came from Korea. Underscore, a data team working with the MBC documentary series PD Note, analysed 10.4 million postings from one large Korean job platform, July 2019 to January 2025, using blue-collar postings, the least exposed to generative AI, as the control. After December 2022, new-graduate postings for developers fell 33.5% relative to that control, and digital design fell 20.6%. Postings for experienced hires showed no significant change. The experience required of developers rose from 3.73 years to 5.18. In a companion interview on the twenty-second, the analyst Kang Tae-young said the decline began in summer 2022, before ChatGPT, as interest rates rose and the pandemic hiring boom unwound; the authors say those effects cannot be fully separated from AI. It is one platform, postings rather than hires, and not peer reviewed. The same interview reported a survey of 1,700 Korean adults: about 46% use AI often, and 8% pay for it.

How firms measure

The Russian marketplace Avito gave the clearest account of changing the metric. In a talk uploaded on the twentieth, Alexander, head of platform development, whose surname is garbled in the captions, said Avito dropped "minutes saved" metrics in February 2026. Saved minutes added up to a free hour per developer per day, which dissolved into tech debt and comfort, with "no business effect by default". Avito now tracks significant production increments per constant headcount. The 2026 target, a 15% cut in cycle time, was beaten by July. Increments rose more than 30%, but after the normal success rate of product changes, he put the business gain at single-digit percent, and named code review, product discovery and communication as bottlenecks. He put the practical limit at about five parallel agent sessions per person. All figures are self-reported.

Review and accountability inside firms

Avito's talk carried the line of the week on review. Pull requests nearly doubled, yet review time fell. The speaker's reading: people simply look less. Avito now makes the author own what ships and has a model reviewer flag priority comments a person must read.

Tacit knowledge into systems

The DataOps team at the Korean company Toss published a post on the twenty-third about turning log-definition rules held in people's heads into a shared AI skill. The skill reuses existing specifications and proposes new ones for a person to confirm, while mechanical rules stay in a command-line tool queried at run time. The team split its documentation per task after the model missed rules buried in one long document. No adoption figures.

What to watch

  • Whether METR verifies OpenAI's monitoring fixes, and whether other labs publish monitor-coverage figures.
  • Kimi K3.1 Pro weights (promised by 4 November) and Mistral Large 4 weights (reported for 27 October).
  • Whether more open-source projects follow curl and Firefox in changing disclosure rules.

Sources filed this week (119)

Open Week 43 in the Library

Every source screened and kept this week, strongest first. Each links to its Library page with the digest and the reason for its grade.

A Strong evidence (42)

B Useful, limited (70)
C Weak signal (7)