The vendor demo makes fine-tuning look like installing software - upload data, train, deploy. The real distribution of outcomes is brutal, most of the work is data preparation, and the model you trained for wire fraud can quietly forget how to read ACH. For financial services, guessing is not an option.
Most large models keep every parameter lit for every token, like a city that never turns the lights off. SpikingBrain borrows the brain's trick - fire only the neurons that matter - and reports order-of-magnitude efficiency on Chinese MetaX chips. The headline numbers are real, with conditions worth reading.
The AI race everyone watches is the application layer. The one that decides it runs four layers down, in chip design, fabs, and export controls. When the US cut China off from the best GPUs, DeepSeek answered with efficiency instead of scale - and that is the lesson worth keeping.
GPT-5 ships as one model with an automatic router that decides how hard to think for you. Convenient, until you watch a simple question quietly trigger a web search, a page read, and a reasoning chain you never asked for - and burn through a capped Plus plan in an afternoon.
Nearly three-quarters of banks claim AI-powered fraud detection, but when a wire has to clear in under 100ms the model steps aside and deterministic rules make the call. The honest architecture is hybrid - and saying so is becoming a regulatory and legal requirement.
Most payment integrations still look like 2015 - custom code per platform, months-long builds. The Model Context Protocol turns MxN integrations into M+N, and payment platforms are starting to ship it. The promise is real; so are the enterprise security gaps.