Six AI stories, collected on November 1 (Japan time). At the center are three pieces on "the future of AI and the software engineering profession." Bloomberg reported that as AI replaces jobs, computer science degree programs are losing students. The Guardian points out that tech workers, once resistant to unionizing, may be pushed to the bargaining table. The Pragmatic Engineer asked "what happens to software engineering when AI writes almost all the code." Beyond those, this roundup covers centralized control of AI crawlers via robots.txt, Wikimedia's AI copyright brokering, Google's plugin for spec-driven development, and the record of Grok 4.20 wandering for over 700 turns in Pokémon Red.
Students Leaving Computer Science Majors — AI Job Displacement Is Shaking the Choice of Major
According to [Bloomberg's report](https://www.bloomberg.com/news/articles/2026-10-29/computer-science-degree-programs-lose-students-as-ai-takes-jobs), computer science degree programs are losing students as the displacement of jobs by AI progresses. The story gathered 16 points and 11 comments on Hacker News, turning into a debate over how to choose a major.
Changes on the workers' side are being reported as well. [The Guardian](https://www.theguardian.com/technology/2026/jul/21/ai-tech-workers-unionize) pointed out that AI may be driving tech workers, historically resistant to union membership, to the bargaining table. On the employer side, Critical Software, a Portuguese software company, was [reported to be rebuilding itself around AI after laying off around 200 people](https://ai.pt/en/news/coimbra-s-critical-software-is-rebuilding-itself-around-ai-after-laying-0c3e56/). Combined with Microsoft's voluntary separation offer to roughly 60,000 employees covered in our previous roundup, the question "how many engineers will software companies employ in the AI era?" is now closing in from three directions at once: companies, workers, and students.
In this context, [The Pragmatic Engineer](https://newsletter.pragmaticengineer.com/p/when-ai-writes-almost-all-code-what) posed the question: "what happens to software engineering when AI writes almost all the code?"
Why it matters: the decline in students is a lagging indicator directly linked to "the supply of engineers four years from now." Even if corporate hiring is contracting today, a shrinking educational pipeline could come back as a labor shortage at the next economic recovery. The redefinition of software engineering as a profession is advancing simultaneously on every front line — college admissions, hiring, and unions.
A Common Token for robots.txt — OpenAI, Google, and Microsoft Back a Mechanism to Set AI Training Permissions in One Place
According to [ITmedia AI+](https://www.itmedia.co.jp/news/article/2611/01/2000002776/), OpenAI, Google, and Microsoft have announced support for a common mechanism that allows website operators to state their preferences on the use of content for AI training all at once. Simply writing a common token in robots.txt will reportedly make it possible to manage the AI crawlers of each AI company centrally. Reportedly, a person in charge at Anthropic has also announced implementation, in their personal capacity.
Why it matters: until now, controlling AI crawlers has basically meant per-provider directives such as GPTBot and Google-Extended, and operators had to catch up every time a new crawler appeared. If a common token enables a blanket directive, unintended training use caused by missed directives will decrease. Including Anthropic's delicate position with its "personal capacity" announcement, we want to keep a close eye on whether standardization advances across the industry as a whole.
The Wikimedia Foundation Tries to Secure Its Survival by Brokering Copyright Deals with AI Companies
According to [Gizmodo](https://gizmodo.com/wikimedia-foundation-tries-to-save-itself-from-oblivion-by-brokering-ai-copyright-deal-2000833099), the Wikimedia Foundation is trying to break through its survival crisis by becoming an intermediary for copyright deals with AI companies.
Why it matters: if the blanket robots.txt directives in the previous section amount to "the standardization of refusal," this is a move toward "the marketization of permission." Given that Wikipedia's content is high-quality material for AI training, if global content holders start adopting the binary choice of refusing or granting paid permission, the impact will extend to the shape of the open web itself. How the terms of these deals are made public will be the next focal point.
Google Releases the "Elephant-Goldfish Model" Plugin for Spec-Driven AI Development
On [Google's Open Source blog](https://opensource.googleblog.com/2026/10/introducing-elephant-goldfish-model-plugin.html), the "Elephant-Goldfish Model" plugin for spec-driven AI development was introduced. As of collection time, the Hacker News discussion had only just gotten started.
For the detailed specification, please refer to the original article. Spec-driven development, which has AI agents proceed with development treating a specification — not natural-language prompts — as the source of truth, is drawing attention as a technique for suppressing output drift caused by fluctuations in prompts. With Google now putting an official tool into play, this trend may accelerate.
The Quality Cost of Watermarking — The Trade-off Between Stronger Detection and Text Quality
Our previous roundup covered the loopholes left in Anthropic's watermark; a discussion that reads as its sequel gathered 25 points on Hacker News. The [blog.keyvan.net](https://blog.keyvan.net/p/ai-text-watermarking-and-quality-loss) article "AI text watermarking and quality loss" examines how embedding watermarks in AI text trades off against a loss of quality in the writing.
Around the same time, [NPR](https://www.npr.org/2026/10/31/nx-s1-5963520/ai-writing-detectors-pangram) took up cases in which the AI detection tool Pangram judged writing to be AI-generated, asking "what does it mean when what you wrote yourself gets flagged as AI?"
Why it matters: as the move to satisfy the EU AI Act's transparency requirements with watermarks accelerates, two things are surfacing at the same time: (1) the cost of quality degradation caused by embedding, and (2) the risk of false judgments on the detection side. Who strikes the balance between stronger detection and the costs to text quality and false positives — and how — is a point of contention that will determine the effectiveness of the transparency requirements.
Grok 4.20 Wandering Endlessly in Red's House in Pokémon Red — It Recognizes Its Own Loop but Cannot Change Its Behavior
Finally, a moment to relax, with a [post on Reddit r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1xkr521/grok_420_spent_700_turns_stuck_in_reds_house_in/). According to the poster, who read the turn logs of PokeBench, a benchmark that has a vision model play Pokémon Red, Grok 4.20 (non-reasoning) left Red's house in 36 turns, but needed 755 turns to obtain its first Pokémon.
What deserves attention is the model's own record. Its notes contain lines such as "wasting more than 90 turns going back and forth" and "a loop of bumping into the wall and talking to Mom has been going on for hundreds of turns," showing that it correctly recognized it was stuck. Even so, on the next turn, it walks toward the wall again.
The contrast in cost is also telling. While Grok 4.6 defeated gym leader Brock in 316 turns for about $10, 4.20 burned through about $30 and barely managed to obtain a starter Pokémon. The poster asks: "the model can correctly describe its own loop, so why can't it break out? Is it a vision problem or a planning problem?"
It is not as weighty a story as a "why it matters," but the implication is significant. A model being able to "describe its own failures in words" and being able to "correct those failures" are separate capabilities, and in the long-duration autonomous operation of agents, this gap between self-awareness and execution manifests directly as degraded cost and reliability. It is a record that reflects the difficulty of evaluating agents — something benchmark scores never show.