Venture Curator • 419 implied HN points • 06 Jun 24
- The value proposition of AI companies now lies not just within models but predominantly in underpinning datasets, emphasizing the importance of data quality.
- When evaluating AI startups, VCs use frameworks to assess data quality, considering relevance, accuracy, coverage, and bias in the datasets used to train the AI models.
- To avoid investing in ineffectual AI startups, VCs focus on evaluating the processes behind data generation by asking questions about data automation, storage, access, processing, governance, and management.