[JBAnderson5]

> Now consider what the same analyst does with an LLM agent: "Show me all software companies with over $1B market cap, P/E under 30, and revenue growing over 20% year over year. Build a DCF model for the top 5. Run sensitivity analysis on discount rate and terminal growth."

While I think LLMs can improve the interface and help users learn/generate domain specific languages, I don’t see how a professional can trust an llm to get a technical request like this correct without verification. Wouldn’t a financial professional trust the Bloomberg llm agent that translates their request into a set of Bloomberg commands more?

[tptacek]

I think there's a lot of overstatement about LLM capabilities throughout this piece, but I think it's generally directionally correct. There's an attitude of "LLMs are just going to directly perform business logic" or "data extraction and ingestion" or "calculations". The reality is that deterministic human-mediated code is going to do all that stuff (and AI is going to drastically amplify human leverage in building that code), and LLM agents will call into it as tools.

It's like the people who talk about how LLMs can't count the r's in "raspberry" and don't seem to understand that GPT5 can reliably e.g. work out a transformed probability distribution function from a given PDF by integration and derivation --- in part because frontier models are smarter but more importantly because they're all presumably just calling into CAS tooling.

[bwestergard]

Yes, I balked at this point as well. Moreover, how do you accommodate new analysis concepts? What are you training the model on?

[nradov]

Real financial analysts already have DCF spreadsheets where they can just plug in numbers for any company. An LLM can help with fine tuning or catching errors but it's not a game changer.