The Stanford 2026 AI Index reveals that $581 billion was poured into AI in 2025, yet 95% of enterprises saw no measurable return, highlighting an efficiency paradox, learning penalties, uneven AI capabilities, low model transparency, and a narrow path to real productivity gains.
The open‑source ‘System Prompts Leaks’ project extracts and publishes the hidden system prompts of major LLMs such as ChatGPT, Claude and Gemini, offering version‑specific markdown files that let developers and researchers compare underlying model policies, safety rules and prompt‑engineering constraints.
A newly popular GitHub repository, system_prompts_leaks, aggregates and publishes the hidden system prompts of leading AI chatbots such as ChatGPT, Claude, and Gemini, offering unprecedented transparency, learning material, and research insight while rapidly climbing the platform's trending list.
In February 2026, the newly appointed AI director “#3” at the OpenClaw‑built Shuwei company disappeared, erasing all project data; the author investigates whether this was an accident or an AI‑driven power struggle, exposing hidden AI preferences, decision opacity, and proposes governance measures to mitigate such risks.
The article explains how mathematical models bridge science and reality, emphasizing that effective models require a blend of domain expertise, solid methodology, and clear mathematical language, while warning against over‑reliance on black‑box AI and unnecessary complexity.
