Quoting Tom MacWright

2026-06-24 · Source: Simon Willison's Weblog · Field: Business & Management — Human Resources & Workforce Development · Depth: Fundamental Awareness, quick

Summary

On June 24, 2026, Tom MacWright observed a growing trend in job applications featuring extensive use of Large Language Models (LLMs). He noted applications where resumes were clearly cowritten by LLMs, linking to LLM-generated portfolio sites, which in turn connected to LLM-generated GitHub projects with purely LLM-generated commit messages. MacWright's primary concern is the resulting "accidental anonymity," where applicants fail to convey genuine personal information or unique qualities. He states that these "perfected, generated, prompted" applications are generic and impersonal, revealing nothing about the individual beyond their use of specific AI tools, thus hindering the ability to truly understand the candidate.

Key takeaway

For hiring managers evaluating technical roles, be aware that fully LLM-generated application materials, from resumes to GitHub commits, can obscure a candidate's true identity and unique contributions. You risk overlooking genuine talent if you rely solely on polished, generic submissions. Job seekers should prioritize showcasing their authentic voice and original work to stand out, ensuring your application reflects who you truly are, not just your proficiency with AI tools.

Key insights

Extensive LLM use in job applications creates "accidental anonymity," obscuring genuine candidate identity and skills.

Principles

• Authenticity is lost with full AI generation.
• Generic applications reveal only tool usage.
• Personal voice is crucial for candidate evaluation.

In practice

• Avoid full LLM generation for personal content.
• Prioritize unique, human-authored contributions.
• Showcase individual thought, not just tool proficiency.

Topics

• LLM Applications
• Recruitment
• Candidate Evaluation
• AI Ethics
• Personal Branding
• Job Seeking

Best for: CTO, VP of Engineering/Data, HR Professional, Director of AI/ML, AI Ethicist

Related on AIssential

• I Sent 87 Job Applications and Got 2 Interviews. Then I Changed One Thing. — Modern job applications demand tailored relevance, not just quantity, to bypass ATS and engage recruiters.
• Fragments: March 26 — Complex systems, from AI perception to national security, demand nuanced understanding and robust, verifiable controls.
• Thoughts on the job market in the age of LLMs — The LLM job market prioritizes senior talent for system navigation and junior talent for obsessive, focused progress.
• How Software Engineering Students Use LLMs to Write Research Papers: An Experience Report — Students use LLMs across academic writing, balancing utility for clarity and organization with critical verification of AI-generated content.
• Why Your Employee Feedback Data Might Be Misleading You — LLMs introduce probabilistic variability and specific biases that challenge reliable employee feedback analysis.
• The Revenge of the Data Scientist — Core data science skills are essential for effective LLM evaluation and robust AI system development.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.