First-order effects

• KPMG, Coatue and Headline get faster, wider screening of acquisition targets and startups, turning deal sourcing — historically a relationships game — into a partially computational task where early adopters see targets rivals miss.
• The adoption lands amid open questions about training data: the FT coverage itself surfaces concerns that models trained on historical funding patterns could reinforce existing biases against groups like female founders.

Second-order effects

• Rival funds and advisory firms face pressure to deploy comparable screening tools or concede first look at AI-flagged targets — a dynamic already visible as Accel and Sequoia audit their portfolios for AI exposure while most new Sequoia India deals turn AI-related.
• If many funds converge on similar models and data sources, they risk bidding up the same algorithmically surfaced startups, echoing the crowding PitchBook documented in US AI rounds where a large share of capital concentrated in a handful of companies.

Third-order effects

• If Gartner's trajectory holds, AI screening becomes table stakes and competitive advantage migrates from the model itself to proprietary data and access — the endpoint sketched in Wired's look at agentic investors analyzing pitch decks and teams.
• Reliability becomes the constraint on the pattern: KPMG's own experience retracting an AI benefits report built partly on hallucinated case studies illustrates why AI-generated analysis still needs human verification before it drives capital allocation.