AI’s Roll In Smarter Investments
Merging human insight with AI analytics for smarter early stage investments
Happy Thursday, 👋
Artificial Intelligence (AI) is revolutionizing industries across the board, and venture capital is no exception. Having previously discussed how AI should be viewed as a productivity tool, we now explore how AI is transforming two critical aspects of venture capital: due diligence and deal sourcing.
Our goal is to provide helpful insights that might spark new ideas for those in other industries. Understanding how one sector adapts to emerging technologies often catalyzes similar transformations in different fields.
AI-Enhanced Due Diligence
Traditional due diligence is an intensive, manual process involving deep dives into financial metrics, market conditions, team dynamics, and competitive landscapes. Research shows that comprehensive venture capital due diligence typically requires at least 20 hours per company, often leading to information overload and organizational challenges.
AI has streamlined the diligence process by aggregating data from multiple sources, such as financial reports, market trends, customer discussions, social media, and competitor disclosures. By consolidating and analyzing this data, AI enables us to ask more targeted questions and uncover potential red flags more quickly. While leveraging AI has nearly halved the time we spend on due diligence by improving basic research, in no way does it replace the need for detailed analysis before making investment decisions.
Key AI Applications in Due Diligence:
Natural Language Processing (NLP): NLP allows us to scan and analyze legal documents, patents, and even founder interviews, extracting key risks and opportunities from complex texts. Previously, this was a tedious task often outsourced or handled manually. Now, NLP can review vast amounts of material, such as legal documents, intellectual property filings and academic research in a fraction of the time, highlighting areas that need further investigation.
Predictive Analytics: Based on historical data, AI can predict how similar startups have performed under various market conditions. For example, we can assess how companies in similar sectors fared during economic downturns and use this information to provide a probabilistic forecast of a startup’s success or failure. By continuously feeding data into a large language model (LLM), we build a growing pool of venture data that becomes invaluable when assessing new trends or ideas.
Reducing Bias and Enhancing Objectivity
Human bias inevitably influences decision-making. In venture capital, factors such as founder charisma, demographic backgrounds, and personal networks have historically influenced decisions, sometimes leading to missed opportunities or poor investments.
AI serves as an objective counterbalance, providing data-driven evaluation of startups. Human biases can significantly impact investment decisions, as evidenced by well-known funds investing in charismatic founders despite clear warning signs.
Cautionary Examples of Bias Reduction:
💸 Achieved a $1B+ valuation despite fraudulent practices
🎭 Used fake invoices to inflate revenue growth
🖥 Data analysis might have flagged financial inconsistencies and prompted more targeted questions about accounting practices
Theranos
💬 Narrative strength overshadowed weak Underlying data
🙈 Exemplifies how bias influenced investment decisions by respected venture funds
📊 Demonstrates the importance of data-driven validation
AI-Powered Deal Sourcing
Beyond due diligence, AI also enhances deal sourcing capabilities. We continuously refine our AI tools to scan blogs, academic papers, and industry news, helping predict emerging trends. This aggregated analysis helps identify potential industry inflection points where new technologies and ideas converge.
Successful venture investing is highly dependent on finding teams building companies at the ideal time. We often pose the “why now” question to founders to assess whether their product's timing aligns with external trends. Many teams launch without fully considering how evolving technologies or market shifts might impact their trajectory. AI helps us scan across industries to better anticipate which ideas are likely to gain traction.
The Future of AI in Venture Capital
The integration of AI into the venture capital investment process will continue evolving, particularly in due diligence, deal sourcing, and portfolio management. However, while AI excels in analyzing liquid public markets, venture capital remains a long-term, illiquid investment category.
Human intuition and experience remain crucial alongside AI insights, especially in early-stage investing. Identifying successful ideas requires a balanced approach that combines technological analysis with human judgment. The future lies not in AI replacing human decision-making, but in creating a symbiotic relationship that enhances both technological efficiency and human insight to uncover successful venture capital investments.