Key Learnings from the Affinity Webinar on Early-Stage Investing in the AI Era

As venture capital continues to evolve in the AI-driven era, investors are rethinking traditional approaches to due diligence and early-stage engagement. A recent Affinity webinar offered practical strategies and frameworks that reflect how the industry is shifting.

Specialization is Defining Venture Investing

Venture capital is becoming increasingly specialized across different stages of a startup’s lifecycle. This demands more tailored diligence methods and closer alignment between fund strategy and investment stage. Early-stage investors are particularly focused on the potential of founding teams and their ability to navigate ambiguity during the formative growth phases.

Founders’ Adaptability is Paramount

One of the most critical early indicators of long-term success is the founder's ability to learn and evolve. Assessing adaptability, leadership resilience, and learning agility has become central to early-stage diligence. Reference checks, back-channel conversations, and close examination of past decision-making are used to understand how founders respond to pressure and change.

Product Thinking Over Product Snapshot

When evaluating a startup’s offering, it’s no longer enough to assess the current version of the product. The emphasis is now on understanding how product decisions are made—how teams prioritize features, handle trade-offs, and read market signals. This lens provides richer insight into their ability to navigate pivots and scale strategically.

Engagement Data Beats Early Revenue

In the earliest funding rounds, engagement metrics such as cohort retention, user behavior, and product stickiness often provide more reliable indicators of future success than early revenue numbers. These signals help identify products that are resonating and have the potential to scale sustainably.

AI is Reshaping the Due Diligence Playbook

AI is enabling a new generation of investors to blend instinct with data. Emerging tools are helping source qualitative signals from unconventional sources—social media, forums, and digital footprints—offering a more holistic view of a company’s perception and potential. AI is also streamlining traditional diligence functions, such as:

• Detecting strong founder networks earlier

• Accelerating investor learning curves in unfamiliar sectors

• Analyzing customer cohorts for behavioral patterns

• Benchmarking startups using historical firm-level data

Despite this, elements like reference calls and hands-on product trials remain essential and irreplaceable.

Investment Memos as a Strategic Tool

Investment memos are not just internal documentation—they are strategic tools for reflection and discipline. Capturing the logic behind an investment at the time of decision provides a valuable reference for future learning and performance evaluation. AI may soon assist in automating parts of this process, freeing up time for deeper strategic thinking.

Rethinking Return Forecasts in Early Stages

At early stages where data is limited, the focus is shifting from detailed financial projections to evaluating whether a company has the potential to deliver a “venture-like return.” This change underscores a mindset shift toward strategic framing over rigid modeling.