AI tools enable all candidates to produce polished cover letters, destroying their value as a signal of effort and quality. When employers can't differentiate between good and mediocre applicants, they become unwilling to pay a premium for top talent. This paradoxically lowers wages for the best candidates and erodes overall market efficiency.
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Candidates are embedding hidden text and instructions in their resumes to game automated AI hiring platforms. This 'prompt hacking' tactic, reportedly found in up to 10% of applications by one firm, represents a new front in the cat-and-mouse game between applicants and the algorithms designed to filter them.
Tools like Final Round AI provide candidates with live, verbatim answers to interview questions based on their resume and the job description. This development undermines the authenticity of remote interviews, creating a premium on face-to-face interactions where such tools cannot be used covertly.
Historically, well-structured writing served as a reliable signal that the author had invested time in research and deep thinking. Economist Bernd Hobart notes that because AI can generate coherent text without underlying comprehension, this signal is lost. This forces us to find new, more reliable ways to assess a person's actual knowledge and wisdom.
AI is a key factor in the current labor market stagnation. Companies are reluctant to hire as they assess AI's long-term impact on staffing needs. At the same time, they are holding onto experienced employees who are crucial for implementing and integrating the new AI technologies, thus suppressing layoffs.
When companies use black-box AI for hiring, it creates a no-win 'arms race.' Applicants use prompt injection and other tricks to game the system, while companies build countermeasures to detect them. This escalatory cycle is a 'war of attrition' where the underlying goal of finding the right candidate is lost.
As AI renders cover letters useless for signaling candidate quality, employers are shifting their screening processes. They now rely more on assessments that are harder to cheat on, such as take-home coding challenges and automated AI interviews. This moves the evaluation from subjective text analysis to more objective, skill-based demonstrations early in the hiring funnel.
Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.
AI is exacerbating labor inequality. While the top 1% of highly-skilled workers have more opportunity than ever, the other 99% face a grim reality of competing against both elite talent and increasingly capable AI, leading to career instability.
Job seekers use AI to generate resumes en masse, forcing employers to use AI filters to manage the volume. This creates a vicious cycle where more AI is needed to beat the filters, resulting in a "low-hire, low-fire" equilibrium. While activity seems high, actual hiring has stalled, masking a significant economic disruption.
AI disproportionately benefits top performers, who use it to amplify their output significantly. This creates a widening skills and productivity gap, leading to workplace tension as "A-players" can increasingly perform tasks previously done by their less-motivated colleagues, which could cause resentment and organizational challenges.
