An anonymous reader quotes a report from NPR: The buzz on college campuses is that AI is disrupting the job market for young college graduates. But new research from the Federal Reserve Bank of New York finds that the culprit may be something else: remote work. An analysis of federal employment data, paired with a deep dive into the flexible work arrangements at one unnamed Fortune 500 tech company, reveals that companies are less likely to hire recent college grads into occupations that can be done remotely. Researchers speculate that employers are reluctant to put such workers in a setting where it's harder to absorb lessons from coworkers. The researchers found the unemployment rate among younger college grads -- those under the age of 29 -- rose 20% after the pandemic, while unemployment among older college grads fell slightly. The study compares unemployment rates pre-pandemic, from 2017 to 2019, with unemployment rates after the pandemic, from 2022 to 2024. Unemployment rose as remote work grew fourfold, the researchers write. "Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees." Regardless of the cause, the New York Fed report warns that a high unemployment rate among young college grads is concerning. "Early-career experiences can have lasting consequences," the researchers write. "Research finds that individuals who began looking for jobs in slacker labor markets tend to have lower earnings and slower career progression relative to comparable peers who began their job search in better market conditions." Further reading: Why Is the US Job Market So Tough, Especially for Recent College Grads?

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A historian-turned-software engineer warns that "so little is ever written down" by professional programmers in a new article for Fast Company: Perhaps there's an early design doc, but then it turns out that everything was substantially revised before work began. Maybe there are a few wiki pages explaining known issues, some of which were solved a long time ago and others that have been left to molder in the codebase. Somebody might have left a comment in the code itself, but typically it's a warning not to change something or else something else will break... Software engineering has an ambivalent relationship with documentation. Everyone agrees documentation matters in theory, but in practice it's inconsistent, outdated, or missing entirely. Part of that is simple inertia. Writing documentation is usually less interesting than writing the code itself. But it's also ideological. The Agile movement emerged in part as a reaction against the heavily documented Waterfall methodology, and one of Agile's core values explicitly prioritizes "working software over comprehensive documentation." In escaping bureaucratic overdocumentation, the industry also normalized underdocumentation. High turnover at software jobs always brings "a constant drain of domain knowledge." And he's he's skeptical that generative AI will be able to fill in those gaps: [H]aving it generate documentation on the codebase itself might sound like a solution to the absence of other written information. LLMs can certainly summarize code back to you. But hold up with that idea. Beyond hallucinations, there's a deeper problem: Writing documentation is itself part of the thinking process. Whether I'm writing history or software, putting an approach into words helps refine it before I sink hours into implementation. Documentation also captures intent. An LLM may be able to summarize what a codebase does, but it cannot reliably explain why a developer chose one approach over another, or what trade-offs shaped that decision... An LLM can read code that I've written. It might even scan a large codebase and accurately summarize what it's doing. But it can't assess authorial intent. Thanks to long-time Slashdot reader smooth wombat for sharing the article.

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