Google just dropped a compression algorithm that makes AI 8x faster while using 6x less memory. Zero accuracy loss. It's called TurboQuant. Here's why it matters in plain English: Every time you talk to ChatGPT or any AI, the model has to remember everything you've said in the conversation. That memory is called the "key-value cache." The longer the conversation, the bigger the cache, the more expensive it gets to run. This is the single biggest bottleneck in AI right now. A 128,000-word conversation on a large model eats 40GB of GPU memory just for that one user. Scale that to thousands of users and you're burning millions in compute costs reprocessing the same data over and over. TurboQuant compresses that memory down to just 3 bits per value (from 32 bits) without losing any quality. Independent developers tested it within hours and got exact matches against full-precision output. What this actually means: - AI models that needed a $10,000 workstation could now run on a MacBook - Always-on AI agents become dramatically cheaper to operate - Open-source models that were too big for consumer hardware suddenly fit - The cost curve for every company running AI infrastructure just shifted Developers are already porting it to Apple Silicon. No retraining required. It drops into existing AI systems without modification. The AI cost problem isn't being solved by building bigger data centers. It's being solved by mathematicians figuring out how to do more with less.