A small (but important) reminder on AI in practice
Came across this recent story where a cybersecurity report had to be withdrawn after it was found to contain AI-generated "hallucinations" including references that didn't actually exist.
It's a useful moment for all of us working at the intersection of investing, technology, and data. We're seeing rapid adoption of AI across research, due diligence, and reporting and rightly so, given the efficiency and analytical upside it brings. But this is a good reminder that AI outputs are only as strong as the controls, governance, and human oversight around them.
From an investor perspective, a few questions worth reflecting on:
- How are firms validating AI-generated insights before they inform investment decisions?
- Where do we draw the line between efficiency gains and model risk?
- What does "trust but verify" look like in an AI-enabled workflow?
- And as AI becomes embedded in portfolio companies, how should we think about operational and reputational risk?
Would be great to hear how others in the community are approaching this particularly in research, reporting, or risk functions
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Aya Pariy
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