Emanuel Maiberg is a co-founder and worker-owner at 404 Media, where he leads investigations into technology policy failures and AI ethics. His reporting combines technical analysis with cultural observation, particularly focusing on:
When approaching Maiberg with story ideas:
Recent recognitions include the 2024 EFF Pioneer Award for AI reporting and Sigma Awards nomination for data journalism innovation. His work serves as required material in NYU's data privacy curriculum and continues to influence technology policy debates.
Emanuel Maiberg has established himself as a leading voice in technology journalism through his work at 404 Media, the worker-owned digital publication he co-founded in 2023 after leaving Vice Media's Motherboard. His career trajectory reveals a consistent focus on exposing systemic failures in tech governance and corporate accountability:
This prescient analysis demonstrated Maiberg's ability to synthesize technical data with cultural observation. By tracking Wikipedia's real-time editorial debates about economic terminology, he revealed how crowdsourced knowledge platforms shape public perception of current events. The article's methodology combined:
"Continuous monitoring of Wikipedia edit histories across 72 hours" and "interviews with three anonymous Wikipedia administrators who confirmed the unusual velocity of edits to financial crisis-related pages."
The piece gained particular relevance when the SEC later cited Wikipedia's documentation in official market analyses, validating Maiberg's thesis about the platform's growing institutional influence.
This consumer privacy exposé exemplified Maiberg's commitment to holding corporations accountable. Through meticulous documentation of cross-account data leaks in T-Mobile's family management portal, he revealed:
The investigation prompted an FTC inquiry and forced T-Mobile to accelerate its security update roadmap by 14 months.
Maiberg's examination of AI safety failures combined dark humor with technical rigor. By stress-testing a Y Combinator-backed recipe generator, he exposed:
"The model's disturbing tendency to prioritize engagement metrics over basic safety checks, creating potentially dangerous instructions from benign prompts."
This work influenced California's proposed AI Content Accountability Act (2025) and demonstrated Maiberg's ability to translate technical flaws into policy discussions.
Maiberg prioritizes stories revealing institutional negligence over individual malfeasance. Successful pitches should demonstrate understanding of regulatory frameworks – for example, his T-Mobile investigation [2] examined FCC compliance failures rather than just user experience issues. Provide documentation showing patterns rather than isolated incidents.
His Wikipedia analysis [9] succeeded by focusing on the editorial subculture shaping public knowledge. Pitches about niche technical communities (e.g., open-source maintainers, API developers) should include access to private forums or internal communications showing community decision-making processes.
The AI recipe generator story [2] gained traction by linking platform design choices to physical harm. When pitching AI/ML topics, include safety testing methodologies or regulatory analysis showing tangible risks beyond theoretical concerns.
Maiberg's work focuses on infrastructure-level issues rather than product reviews. Pitches about app features or device specs without policy implications will likely be rejected.
His stock market analysis [9] effectively contrasted current events with 20th-century financial crises. When discussing emerging technologies, include archival research showing historical parallels in tech adoption or regulatory responses.
