A VC explains an indispensable daily use case for AI: inputting the names of people attending a dinner or meeting and having the AI generate bios and backgrounds. This replicates a key function of a chief of staff, leading to richer, more informed conversations.

Despite having a large staff, Gary found crucial context was lost from meetings. He now uses an AI tool as a "capture all" CRM, sending it photos and notes via text. The AI builds a relationship graph that he then uses to automate follow-ups and maintain connections, essentially scaling his personal memory.

The Atlantic CEO Nick Thompson turns hours of meeting prep into a single hour using AI. He prompts it to pull bios of attendees, suggest relevant questions, create digital flashcards with names and companies, and then quiz him. This specific workflow ensures he arrives fully prepared for important networking events.

Veteran investor Jason Lemkin argues that the quality of a top founder can be identified without a live conversation, based on asynchronous interactions like cold emails. Having closed multiple billion-dollar exits from such inbounds, he suggests AI could replicate and scale this initial screening process effectively.

Instead of only investing in tech, Sequoia builds it. The firm employs as many developers as investors to create proprietary tools. This includes an AI system that summarizes business plans, analyzes team quality, and maps competitive dynamics, giving partners an immediate, data-rich overview of opportunities.

CEO Brad Jacobs uses AI to automatically take notes and generate summaries from important meetings across his company. This technology provides him with near-instantaneous, unfiltered insights into operations and challenges that previously would have taken months to surface through the corporate hierarchy.

An Executive Assistant at Zapier built an AI agent that automates her weekly meeting prep. The agent researches external attendees, checks the CRM and internal comms for context, and delivers a digest and tasks. This saves hours of manual work and ensures thorough preparation.

Venture firms are building their own small language models trained on internal meeting notes and application data. This allows them to retroactively analyze deals they passed on to refine their investment thesis and identify companies for potential late-stage investments.

Auren Hoffman predicts that by late 2026, the initial VC screening process will be automated. A VC's AI agent will "meet" a founder's AI agent to exchange information and assess fit, making the process more efficient before any human interaction occurs.