Laney surveys how data, analytics, and AI are reshaping venture capital's traditionally intuition-driven process, citing a Data-driven VC report projecting that by 2025 over 75% of VC deal analyses will be informed by data and AI. He points to concrete platforms already doing this: EQT Ventures' Motherbrain and Tribe Capital's Termina use proprietary data to source and evaluate startups while also generating actionable insights for founders, and the AngelList Early-Stage Quant Fund blends a data-centric strategy with qualitative human assessment rather than replacing it outright. The piece frames three areas being transformed: deal sourcing, due diligence, and portfolio management, while cautioning that high development costs, data quality dependence, and the risk of overweighting quantitative signals over human judgment remain real tradeoffs.

Founders raising a round get a useful heads up that the investors reviewing their pitch may already be running their metrics through automated scoring systems before a human ever reads the deck, which changes how founders should think about the data they proactively share and how consistent that data needs to be across every investor facing channel and touchpoint they can actually control themselves right now. LPs and emerging fund managers evaluating whether to build or buy data infrastructure get named, real world examples, Motherbrain, Termina, and the AngelList Quant Fund, rather than abstract claims about AI transforming venture capital. The explicit caution against overweighting quantitative signals is also a useful counterbalance for any fund considering a fully automated sourcing process.