A new benchmark claim puts a hard number on a familiar fear: when AI writes code, the problem is often not speed, but the steady return of old weaknesses at industrial scale.
An Australian argument for compact, sovereign models is really a story about control: who owns the data, where the model runs, and how much risk follows when intelligence becomes local.
A DigiCert survey points to a hard truth for enterprise security teams: AI risk is already showing up in day-to-day operations, and the control problem is bigger than one model or one tool.
Aon’s latest study points to a familiar cybersecurity pattern: technology adoption is speeding up faster than the skills, governance, and employee systems needed to control it.
Australian organisations are being pushed toward more autonomous and proactive cyber defence as AI speeds up and complicates the threat picture.
A study-focused warning about AI agents and admin credentials points to a deeper problem: many organisations may be adding automation faster than they can recover trust in identity systems.
When a modern AI is challenged to code for retro hardware, the results are as enlightening as they are unpredictable.
Lorsqu’une IA moderne est mise au défi de coder pour du matériel rétro, les résultats sont aussi éclairants qu’imprévisibles.