OpenAI's latest study explores the necessity of executable world models, simplification, and verification in coding agents, revealing surprising results. The research evaluates four nested Codex-based agents, finding that every agent variant improves with stronger models and greater reasoning effort. The study's findings have significant implications for the development of Artificial General Intelligence (AGI).

OpenAI is transforming its flagship ChatGPT model from a conversational assistant into a dedicated workplace AI agent, signaling a major shift towards deeper enterprise integration and autonomous task execution. This move positions AI as a proactive 'employee' capable of enhancing productivity across various business functions.

OpenAI's GPT-5.6 Sol Ultra has solved the 50-year-old Cycle Double Cover Conjecture, a fundamental problem in graph theory. The proof was generated in under an hour using 64 subagents working in parallel.

OpenAI is facing significant user backlash over the recent launch of ChatGPT Work and GPT-5.6 Sol, admitting "we didn't get everything quite right." Users reported rapid usage limit exhaustion, a confusing desktop app, and degraded multi-agent workflows, prompting OpenAI to scramble for urgent fixes to UX and cost clarity.

A groundbreaking MIT study reveals how labeling AI agents as 'employees' leads to worse human oversight, as companies like OpenAI push agentic AI tools. New research shows a 18% drop in error detection when AI work is framed as coming from 'digital coworkers.'

OpenAI and Sceye lead groundbreaking advancements in AI collaboration and stratospheric internet. Discover how these innovations are reshaping workplaces and global connectivity.

Anthropic unveils Claude Sonnet 5, a groundbreaking AI model offering enhanced agentic capabilities at lower costs. Targeting developers and businesses, the model aims to outperform competitors like GPT-5.5 and Gemini Pro while reducing operational expenses.

OpenAI introduces ATHENA-R1, an AI agent for treatment reasoning that outperforms language models and tool-use systems. Trained on 212 biomedical tools, ATHENA-R1 achieves 94.7% accuracy on open-ended drug reasoning and 82.9% on treatment reasoning. This breakthrough has significant implications for the healthcare industry and AI research.