The article highlights how the rise of AI technologies, particularly large language models (LLMs), has fundamentally altered the job application process and other areas reliant on written communication. In the past, applying for jobs required significant effort, which acted as a filter for genuine interest. Now, candidates can generate tailored resumes and cover letters in bulk, leading to an overwhelming number of applications. This flood has not improved the quality of hiring; instead, recruiters struggle to find the right candidates amid a sea of generic submissions. A recent graduate's experience illustrates this issue, as they faced months of silence despite their efforts to engage with the job market. The concept of Jevons' Paradox is central to the argument. As the cost of producing written content drops due to AI, the expected quality and meaningfulness of that content diminishes. For instance, research from Freelancer.com shows that the value of well-crafted proposals has decreased significantly since the introduction of AI tools, with employers now less responsive to quality writing. This shift is echoed in various domains, such as product reviews and school admissions, where automated content has led to increased volume but lower trust and effectiveness. The author introduces "Goodhart's inbox," where metrics that once signaled quality have become the targets themselves. As a result, the focus shifts from genuine effort and competence to merely meeting superficial criteria. With the proliferation of polished but interchangeable messages in cold outreach or application essays, the true essence of communication and effort gets lost. This loss of friction is not just an inconvenience; it undermines systems by making it harder to match the right people with the right opportunities, leading to poorer outcomes across various sectors.