The article discusses how coding agents are transforming AI research within OpenAI. It presents early data on agent usage, experiment velocity, task complexity, and the resulting acceleration of research. The piece highlights the growing role of these agents in speeding up development and experimentation.
Anthropic’s top AI model is struggling to attract users even as cheaper alternatives thrive. The company’s July revenue is projected at $65 bn, up from $47 bn in May, and it expects Q3 profitability while boasting 6,000 high‑spending customers. In contrast, OpenAI’s revenue has risen 35 % this quarter, spurred by GPT‑5.6, and a Ramp AI index shows Anthropic’s newer models (e.g., Fable) are less popular than older ones like Opus 4.8.
Accelerating engineering cycles 20% with OpenAI.
Simon Willison discusses how the U.S. Census Bureau’s Business Trends and Outlook Survey has changed its AI‑adoption question wording, leading to a marked rise in reported AI usage among large companies. Using updated October 2026 data, he shows that firms with 250+ employees now report AI use in any business function at about 40%, up from roughly 12% in September 2025 when the question focused on producing goods or services. He notes that this shift coincides with the Bureau’s compliance with Executive Order 14434, the so‑called Super Intelligence order.
GitHub Octoverse 2026 reports a dramatic rise in activity, with merged pull requests tripling to 220 million per month, commits more than tripling to 7.38 billion per month, and new repositories doubling to 30.3 million per month. The report also provides industry‑specific data and highlights repositories focused on interoperability or reverse engineering. Methodology details include country‑level push adjustments using LLM‑derived ISO codes and NAICS sector classification via a multilingual embedding model.
It’s been a year of dramatic change and growth at OpenAI.