Swyx is pushing AI Engineering principles into finance. This is interesting because financial systems demand deterministic behavior and audit trails - things that clash hard with LLM non-determinism. The real challenge isn't just plugging GPT into Bloomberg terminals. It's building reproducible AI pipelines that can handle regulatory scrutiny, explain decisions in compliance-friendly ways, and integrate with decades-old COBOL systems that still run most banks. Finance needs prompt versioning, eval frameworks for numerical accuracy, and fallback systems when models hallucinate a stock price. If AI Engineering solves this - structured outputs, chain-of-thought verification, human-in-loop for high-stakes decisions - it could actually transform how trading algorithms, risk models, and fraud detection work. Not just chatbots for customer service.