Arpitrage • 470 implied HN points • 09 Feb 26
- Finance work is mostly about processing large volumes of documents, and building pipelines to extract, index, and semantically understand those texts lets teams scale research, compliance, and automated actions. You still need provenance, governance, and clear workflows so those outputs are trustworthy.
- AI abilities are uneven: it can boost accuracy and productivity on tasks inside its capability frontier but can hurt performance outside that frontier, so humans need to stay engaged with clear roles (e.g., dividing work or iterating together). This also means guarding against cognitive complacency as tools get easier to use.
- Hallucinations are a core risk with LLMs, and the practical fix today is grounding models with retrieval-augmented generation (RAG) that pulls answers from a curated corpus. RAG reduces made-up claims but doesn't eliminate errors, so high-stakes outputs still require human verification.