Train GPT-2 on Shakespeare and ask it the capital of France - it forgets Paris. That is catastrophic forgetting, one of several failure modes that surface the moment fine-tuning meets production, especially in regulated banking where "the model flagged it" is not an answer.
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.
A clean virtual-account hierarchy looks simple until an HCM client asks you to reverse payroll for 200 employees across three states from three weeks ago. Multi-entity payroll is where account architecture meets 50 sets of wage law - and where instant rails do not yet fit.
Digital twins began as 3D models of physical space. The next step is simulating how a bank actually runs - so you can test a payment-rail change or a regulatory shift against every system before it touches production. Most banks still only document what exists.
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.