Key insights
- The post questions the value of source traceability in AI-generated financial research. While the author prioritizes auditability by linking figures to original sources, they are unsure if analysts truly value this feature or primarily focus on the output's logic and accuracy. The discussion explores the trade-off between trusting AI-generated results and the need for verification, potentially impacting the adoption of AI in fundamental analysis.
Been thinking about this a lot while working on a side project.
Most AI finance tools state numbers confidently with no way to verify where they came from. The assumption seems to be that if the output looks right, people won't dig into the sourcing.
I took the opposite approach, tagging every figure back to its source: SEC EDGAR filing, financial data provider, or a calculated value with the formula shown.
The idea was that an analyst should be able to audit the report, not just read it.
But I'm genuinely not sure if that matters to people who do serious fundamental analysis, or if it's just making me feel better about the methodology.
A few honest questions for this community:
- When you're reviewing research, do you actually trace numbers back to filings, or do you trust the output if the logic holds?
- Is a fully sourced first draft useful, or does it just give you more things to double-check?
- What would make AI-generated research actually trustworthy to you?
Happy to share what the output looks like if anyone's curious.