<aside> šŸ‘¤ Author: Valentin Testa (Intern - Kalei Ventures) Last update: 24/09/2024

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DDVC (Data Driven Venture Capital)

Data-driven venture capital firms, known as DDVCs, utilize artificial intelligence and data to optimize and enhance their processes. These firms believe that AI will enable them to collect data more comprehensively, process it more efficiently, and ultimately make more effective decision.

<aside> šŸ“² Table of Contents

  1. Introduction
  2. Classification of Industries and Sub-Industries
  3. **The State of the Art in DDVC**
  4. Relevance of the AI Driven Vc Sector in Latin America
  5. DDVC Case Studies
  6. DDVC Workflow
  7. Voices of the Industry: Insights and Perspectives
  8. Resources </aside>

1. INTRODUCTION

Definition of the sector

Venture capital (VC), is a form of private equity and a type of financing that investors provide to startup companies and small businesses.

Data-driven & IA VC firms (DDVC), fulfill all of the following criteria: At least one engineer (which summarizes developers and data roles of any kind) in the team; Proven to develop internal tooling in at least one of the segments across the VC value chain.

What is an AI-Driven VC?

AI-driven venture capital firms (DDVCs) utilize artificial intelligence and data to optimize and enhance their processes. These firms believe that AI will enable them to collect data more comprehensively, process it more efficiently, and ultimately make more effective decisions. This allows fewer people to accomplish more tasks with higher quality outcomes.

DDVCs are most prevalent in the $450M - $5B Assets Under Management (AUM) cohort, indicating a sweet spot of available resources and the impact of dedicated engineering initiatives.

Typically, these teams consist of:

For VCs with less AUM, there is a tendency to invest in earlier phases, where the higher deal volume can significantly benefit from DDVC initiatives, though these firms may have fewer resources for dedicated initiatives.

Why should VCs become more data-driven?

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Efficiency

Venture capital firms use artificial intelligence (AI) to improve their work. With AI, they become much more productive. Their work results are better and more consistent. Even the teams that weren't as good improve thanks to AI. Also, tasks are completed faster, almost twice as fast as before.

Effectiveness

Missing out on outlier opportunities due to incomplete coverage and wrong prioritization is a significant issue. According to Mike Arpaia, Managing Partner at Moonfire, ā€œIf you want to improve your decision quality at a faster rate, you need to engage with rigorous decision science. Basically - if you want to outperform for a long time, you need to invest in data, science, and technology.ā€

Inclusiveness

Talent is distributed equally, but capital is not. There is a significant disparity in funding across the world, circumstances often dictate whether someone can become an inventor.

White children are three times more likely to become inventors than black children. Less than 20% of inventors are female, and differences in ability do not account for this disparity. Top-performing children are much more likely to become inventors, but this is predominantly the case if they come from high-income families.

According to Dealroom, from 2017 to 2023, North America received 52 times more venture capital funding per million people than Latin America.