AI Drift Log: Funding Data Drift Across Models — January 2026
Startup funding data is one of the most volatile fact categories in LLM outputs. We tracked funding-related answers for 50 startups across four models over 30 days and found significant inconsistencies.
Funding data accuracy
- Correct funding round identification: 62% across models.
- Correct funding amount: 48% across models (±20% tolerance).
- Correct lead investor: 55% across models.
- Most common error: confusing Series A and Series B rounds.
Drift over 30 days
- 23% of funding-related answers changed during the monitoring period.
- Some models oscillated between correct and incorrect amounts week-to-week.
- Startups with Crunchbase + structured profile: 81% accuracy (significantly better).
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