AI and data-driven approaches are revolutionizing VC decision-making and operations. 

Last week, Dr. Andre Retterath published the Data Driven VC Landscape report. Being at the forefront of VC innovation on the SPV and fund structuring side, here are our thoughts on the technological advances changing the way VC operates.

By leveraging machine learning algorithms and data VC firms are enhancing efficiency, optimizing portfolios, and unlocking higher returns. Using AI and rethinking the approach to allocating capital is no longer a ‘nice to have’: it’s a necessity to remain competitive as a VC.

Read through to learn how to unlock these benefits for yourself, and discover the tool stack to take your VC to the next level.

Click here for the full report.

“Talent is distributed equally. Capital is not” -Dr. Andre Retterath

A data driven, dev first approach allows VCs to improve their efficiency, effectiveness and inclusiveness. VCs are busy. Back to back meetings leave little time for complete coverage and optimal capital allocations. Using a data driven approach enabled by AI can give you more time to focus on making more, fairer, and better deals.

1. Efficiency 

VC investing is a process where you input time to output deals. The secret here is to find ways to input less time, but get more deals more out of your effort. But being constantly distracted means VCs don’t have the time to focus on actual value drivers. This is where AI shines: using AI, VCs can decrease the time needed to perform tasks, increase their output quality, and allow smaller teams to achieve more.

2. Effectiveness 

Returns in venture capital follow a power law distribution. This means there is a very high cost to missing outliers. Luckily, machine learning models have been found to outperform human investors in screening. By using an automated data capture & screening system to monitor and triage through news, social media, cutting edge research etc, you can cut out the noise and start to track the metrics with tangible impact. With this, VCs are getting a higher deal coverage and lowering their miss rates.

3. Inclusiveness

As a result, with more & better informed deals, the wide disparity in funding seen across the world can begin to narrow. Less than 20% of investors are women, and female founded companies represent 2% of global deal volume; despite women investors and founders being shown to outperform the broader market. Data driven initiatives help reduce bias and make better investment decisions. When Vauban joined Carta, we were united by our mission to create more ownership and achieve equity for everyone. These tools can help the VC ecosystem reach that goal.

Only 1% of venture capital firms currently have internal data-driven initiatives. But 84% want to increase their efforts and resources in getting there. That means this is your chance to get ahead of the curve. Here’s how to build your VC to be future-proof. 

Unsurprisingly, to become a cutting-edge VC, you need a team comfortable with tech. VCs need at least one in-house engineer to start a data driven approach. Remember that engineering scales very well, which means AUM per engineer climbs quickly in large funds. The most data driven VCs will grow their engineering teams considerably. The leader here is Level Ventures, with around 75% of the team being engineers. 

A team of engineers dedicated to building large scale data-driven infrastructure will go a long way. Here are some ways every VC and angel investor can implement some degree of AI and data-driven approaches into their operations.

The first thing you’ll want to do is make sure your tech stack is as up-to-date and optimized as possible. The days of excel spreadsheets are mostly over. Here are our top recommendations of AI tools for VCs:

Data source and lead-gen 

Synaptic: Synaptic is a complete advice and due diligence research tool aimed at providing compliant financial advice.

Specter: Specter tracks the entire market on one platform. From market trends, search trends, social trends, companies, talents, websites, and more. 

Proxycurl: Build and scale data-driven applications on people and companies. 

SimilarWeb: Analyze traffic to find the most effective tactics for each platform.

Startup Databases and Research

Crunchbase: Data on private companies to help find your next investment. 

Pitchbook: Large and comprehensive database on the private and public markets.

Dealroom: Data platform to get intelligence on startups. 

CBInsights: Analyze data points on venture capital and startups.

CRM, Portfolio & LP Management

Affinity: A relationship intelligence platform that helps dealmakers find, manage, and close more deals. 

Vestberry: Portfolio management software made specifically for the venture capital and private equity sector. 

Airtable: Airtable allows you to bring all your data onto a single platform and build workflows with near infinite possibilities. 

Carta + Vauban: Spend more time on deals and less time managing your LPs and portfolio with Vauban and Carta.

Productivity

Bardeen: Automate repetitive tasks in one click. 

Calendly: A scheduling platform that makes finding time easy. 

Notion: An all-in-one workspace with tools for just about everything. 

Zapier: No code workflow automation to save precious hours in your week.

Copy.ai: An AI assist for writing blogs, emails, social media posts etc. 

Midjourney: AI generated images for all your content and events.

See Andre Retterath’s full list of 400+ tools for a complete VC tech stack here.

The VCs leading the way in data-driven investing are sharing their insights with their community. Make sure to follow them to learn as you go! Here are some of our recommendations, in no particular order:

Sarah Guemouri, VC, Atomico. 

Stephane Nasser, Founder, OpenVC. 

Ali Tamaseb, VC, DCVC. 

Alexis Robert, Kima Ventures 

Maximilian Fleitmann, Co-founder, Wizard Ventures.

Whether you're an established venture capitalist or an aspiring angel, it's time to explore the possibilities that AI and data-driven approaches offer.

Click here for the full report.