Exploring the limits, opportunities, and risks of generative AI
US cybersecurity jobs are shrinking, but AI security roles are booming—here’s why seasoned assurance engineers still matter and must adapt.
Explainer of MIT Technology Review’s Oct. 19, 2026 interview on Nick Cammarata’s claim that AI models may be more interpretable than once thought.
Why Anthropic’s Opaque Systems deal matters: confidential computing, verifiable AI, and stronger enterprise security for sensitive data.
Can “tax the rich” fix France’s budget crisis? Compare how France, Europe, and the US tax high earners, wealth, and the ultra-rich.
A close read of EY’s 2026 AI survey report suggests human-made data, but likely AI-shaped prose—especially in summaries, transitions, and takeaways.
A forensic analysis of EY’s 2026 AI governance report suggests 70–90% of its narrative prose was likely AI-drafted or heavily machine-assisted.
A sharp take on the AI doom loop: why enterprises are retreating from workplace slop, and what that means for AI-assisted analysis.
Why AI hype echoes open source’s past: prompt injection, weak instruction/data separation, and growing security risks still outpace the pitch.
Companies are cutting back on AI after costly mistakes, error-filled output, and “productivity” loops that create noise, risk, and brand damage.
A sharp, satirical guide to AI companies as iconic songs, skewering OpenAI, DeepMind, Anthropic, Meta, DeepSeek, xAI, Mistral, and Alexa.
A witty satire on Mistral AI, Serge Gainsbourg, and American chatbots, contrasting French elegance with loud, meme-fueled AI culture.
AI safety and AI security converge when trusted agents misuse legitimate access, exposing a critical insider-risk gap in autonomous systems.
AI safety and AI security are not the same. Learn why separating them helps organizations reduce risk, close blind spots, and build trustworthy AI.
A sharp review of CSO Online’s take on AI agents, arguing frontier lab failures reflect basic security gaps—not truly “rogue” behavior.
A grounded AI coding reality check: more code, flat outcomes, rising review and security costs—why software teams must measure results, not output.
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