
A full-stack approach to making advanced AI more capable, more affordable, and more widely useful.
AI infrastructure creates value through making intelligence more capable, more affordable, and more widely available. A pricing reduction strategy demonstrates this approach, with different model tiers now offering varying costs and speeds to match different task requirements. The system's efficiency comes not just from the models themselves but from improvements to software serving, routing, context management, and tool design working together. Broader adoption and real-world feedback create a cycle where more usage generates more data to improve future systems, and better systems expand what tasks become economically viable.

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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