
As experts have warned for the last two years, some companies — like Microsoft and now Google — are finding and patching an exponential number of bugs in their products, thanks to the use of LLMs and AI tools.
Google used internal AI tools to fix more security bugs in Chrome during two releases in June than it had patched in the previous 23 releases over two years, demonstrating a dramatic shift in how quickly vulnerabilities can be discovered and addressed. Cybersecurity experts have predicted that AI systems would accelerate both bug discovery and the need for AI-powered defenses, and this data provides concrete evidence of that trend. Other major tech companies like Microsoft have reported similar exponential increases in bug fixes attributed to their use of AI, though Apple does not appear to be showing the same pattern.

Long-running agents accumulate state that no transcript captures. A coding agent at step 10 holds edited files, a running dev server, installed packages, and a warm prompt cache. When it misreads a traceback and rewrites a file that was already correct, neither available recovery path is cheap: patching forward grows the context and the token bill, and restarting from step one re-pays every model and tool call while reproducing nothing exactly, because runs are non-deterministic. Jumping back t

New AI toolbars and prompts are showing up in Google Docs and Gmail. If you don’t want Gemini’s help in writing documents and emails, here’s how to turn that stuff off.

In October 2025, a storm brewed over the Caribbean Sea. Weather models differed on its trajectory. Would it remain weak and end up in Haiti, or would it intensify and head to Jamaica? Artificial intelligence model WeatherNext, developed by Google’s DeepMind and Google Research, went with the latter. Five days before landfall, it predicted with 80 percent confidence that the storm system would hit Jamaica as a Category 5 hurricane. Hurricane Melissa was catastrophic, causing flooding and landslid
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