AI crawlers hit creator websites & Governance for AI coding assistants - AI News (May 17, 2026)

First up: a stark signal from the creator economy. Axel Rauschmayer, the JavaScript author behind the long-running 2ality blog and several freely readable online books, has temporarily taken the site offline. His explanation is blunt: book sales went from “enough to live on” in 2024 to essentially zero by 2026, while traffic to his pages shot up to levels he can’t afford to host—and he attributes almost all of that spike to AI crawlers. What makes this matter isn’t just one site going dark. It’s the collision between two trends: automated scraping at massive scale, and the fragile business model of independent education online. If traffic no longer correlates with revenue—and actively increases costs—then the open web loses the very resources people rely on to learn.

Staying with AI and software development, Microsoft employees have open-sourced a VS Code extension called “AI Engineer Coach.” The headline idea is simple: teams are using AI coding assistants across different tools, but they rarely have a clear, consistent picture of what’s actually happening—what helps, what wastes time, and what patterns lead to messy results. This project tries to turn local usage logs into a single on-device dashboard, with an emphasis on privacy and a read-only posture. It’s also built to flag common anti-patterns—things like weak prompts, poor session hygiene, or brittle context usage—so teams can improve how they work with AI rather than just “use more AI.” The bigger takeaway: AI-assisted coding is moving from novelty to something that needs governance. Not just for compliance, but to keep engineering quality from drifting as more code is produced faster.

Now to open-source security, where AI is changing the workflow in a more chaotic way. curl maintainer Daniel Stenberg says the project has entered what he calls a “high-quality chaos” era of security reporting. Earlier this year, curl shut down its bug bounty after getting swamped by low-quality, AI-generated submissions. But after moving back to HackerOne, the worst of the spam largely disappeared. Here’s the twist: the volume of reports is now higher than ever—roughly double last year’s already elevated pace—and most submissions show signs of AI assistance. Yet the share of confirmed vulnerabilities has rebounded to around the mid-teens percent, which Stenberg says is back to, or even better than, the pre-AI baseline. Why it matters: defenders and maintainers are now racing in a world where AI can help find real issues faster—but can also increase workload dramatically. And if researchers can automate discovery, attackers can too, raising the stakes for patch speed and maintenance capacity across the ecosystem.

Let’s zoom out from software to the physical footprint of AI. Commentary and reporting continue to highlight how the AI boom is accelerating data-center buildouts—and how communities are pushing back. The concern isn’t abstract: data centers draw huge amounts of electricity, and in some places that demand is being linked to higher rates or stressed grids. Water is also emerging as a flashpoint, with reports of local pressure issues tied to heavy consumption nearby. What’s especially notable is the politics forming around it. Public opposition is rising, and the industry response is increasingly defensive—sometimes framing local resistance as illegitimate or trying to restrict how communities can block facilities. The core issue: AI isn’t just a software story. It’s an infrastructure story, and the argument over who bears the cost—financially and environmentally—is getting louder.

On jobs, two signals are converging around one uncomfortable theme: the entry-level ladder may be narrowing. A global survey from Oliver Wyman suggests many CEOs plan to cut junior roles over the next year or two and tilt hiring toward more experienced workers. That’s a reversal from the recent past, when entry-level expansion was more common. At the same time, reporting from The Economist points to growing concern that new graduates are already feeling a weaker market, even while headline employment data doesn’t show a dramatic AI-driven collapse. The theory is straightforward: if AI tools can handle portions of junior work—especially in areas like coding and routine writing—then companies may hire fewer beginners and rely on smaller teams of experienced staff to supervise AI output. Why it matters: this isn’t just about today’s graduates. If fewer people get those early-career reps, the talent pipeline thins, training shifts to schools or individuals, and long-term productivity could suffer.

And education is wrestling with that shift in real time. A study highlighted by Axios suggests grade inflation has accelerated in certain college courses since ChatGPT arrived. Looking at data from 2018 through 2025 at a large, selective Texas research university, the study reports a sharp rise in top grades in subjects where AI can meaningfully assist—like English composition and coding—while lab and studio-style courses stayed relatively flat. Researchers argue this isn’t a subtle effect: AI can help students produce polished work that would have previously taken stronger mastery. The implication is that GPAs may increasingly reflect a mix of AI fluency, assessment design, and enforcement—rather than subject understanding alone. What it changes: schools may need to redesign assignments, clarify what “allowed AI use” means, and shift evaluation toward supervised work, oral checks, or other formats that better capture true comprehension.

Finally, a privacy story that’s hard to ignore. 404 Media reports that a Reddit user advertised a database of more than 150,000 labeled stool photos, allegedly sourced from an AI gut-health app where users upload images for scoring. The reporting says a founder discussed selling access for AI training and other uses, and a sample dataset allegedly included not only images and AI-generated labels, but also user-provided health and demographic information tied to unique IDs. The company’s marketing reportedly emphasized privacy, while in-app terms granted broad rights to use, license, and retain uploaded health data in “anonymized” or model-derived forms—even after deletion. Why it matters: this is a case study in how sensitive data becomes an asset in the AI economy. And it’s a reminder that “de-identified” doesn’t automatically mean “safe,” especially when datasets can be combined and re-identified.