The Atlantic piece captures the immediate friction in tech hiring but underplays how generative AI has structurally dismantled the signaling mechanisms that once allowed labor markets to allocate opportunity based on verifiable human traits rather than polished simulation. Schumacher's pivot from evaluator to AI-detection entrepreneur illustrates a feedback loop now visible across sectors: applicants mass-produce homogenized materials via tools like ChatGPT, employers deploy screening models that favor keyword density over depth, and the resulting data desert leaves both sides without actionable information. A Columbia Business School analysis of resume homogenization shows this compression effect raises baseline quality while erasing idiosyncratic markers such as unusual career pivots or non-linear skill acquisition, patterns historically used by underrepresented candidates to stand out. This extends beyond software engineering into marketing, finance, and even healthcare administration, where LinkedIn data reveals application volumes per posting rising 300 percent since 2022 alongside flat callback rates. What mainstream coverage treats as a temporary efficiency problem is instead an acceleration of power asymmetry; workers lose feedback loops essential for skill development, while firms outsource discretion to opaque models that embed existing biases at scale. Kathleen Creel's arms-race framing connects to earlier platform dynamics seen in ride-hailing and content moderation, where algorithmic intermediation first promised neutrality and then produced concentrated control. The result is not merely inefficiency but a livelihood threat: entry-level pathways narrow for those without elite networks or paid coaching, reinforcing class stratification under the guise of meritocratic filtering.