How venture capitalists are using AI to invest more effectively

By Vital Laptenok , General Partner at F1V

AI can do almost every job in venture capital. This means that a VC firm may only need 1-2 people, and they can still make millions of dollars through investments.

But that's the future. Today, only 1% of the VC funds have internal data-driven initiatives, according to a recent report by Earlybird Venture Capital partner Dr. Andre Retterath . These firms are the pioneers, and soon, many more VCs will use AI in the same way they do.

AI for sourcing and screening startups

After the ChatGPT's launch, it has become much easier and cheaper to use generative AI models and other technologies. In the past, investors had to hire dozens of people to maintain a high-quality deal flow. Now, using data analysis, AI and machine learning, they can have smaller teams while monitoring thousands or even millions of startups.

About a hundred VC funds, including SignalFire , Episode 1 Ventures , InReach Ventures , and EQT Ventures , have already been using AI tools, according to a study by angel investor Bartosz Trocha . And since there's still no AI software that can tackle all the challenges investment teams face, VCs often develop their own tools.

The most crucial part of the investment process is sourcing and conducting due diligence. These are also the most detailed and time-draining tasks, which AI can simplify and complete in minutes.

To pinpoint promising startups that meet a VC's investment criteria, AI can sift through large datasets, including news articles, social media and pitch decks. After selecting startups, AI can quickly analyze their financial statements, business models, and the market they work in.

London-based VC firm InReach Ventures, which has been using AI to identify potential investments since its foundation in 2015, has created a platform called DIG to streamline startup sourcing and due diligence. It helps the firm find, review and evaluate thousands of European companies each month.

Using generative AI models, the platform collects data from the internet, organizes information about a startup, and then analyzes whether the startup can be a good fit for the VC fund.

InReach Ventures has also connected this tool to its website. This way, the team doesn't get overwhelmed by numerous irrelevant pitches — their software arranges applications in a proper way.

The processing capacity of such platforms can also be much more significant because this kind of tech is scalable. For comparison, EQT Ventures has developed a data-scraping and machine-learning platform called Motherbrain, which can sift through two million companies per day.

It's important for VC firms to have such a tool because it can help them find hidden gems before anyone else does. With its platform, InReach Ventures identified Lithuanian startup Oberlo and invested in it before any other fund even knew about its existence. At that time, Oberlo wasn't actively looking for money.

Shopify acquired Oberlo for $15 million.

AI for classifying startups

Another laborious task for investors is classifying startups after sourcing. If done manually, it can eat up days to scan 400-500 startup summaries. Generative AI models save tons of time, organizing startups into categories according to their stage, market size, business model, industry or other criteria.

Venture Fund Episode 1 has its data platform that gathers and groups about 400 companies for potential investment per week; it draws info from various sources. The team can ask their AI "colleague" to sort companies that match their current focus, like B2B or edtech, and receive a tailored list.

AI for monitoring startups with high potential

As the venture capital industry adheres to the "power law" principle, it can be a big loss for VCs to let a promising investment slip by. When a startup's metrics or profits begin to grow, investors should take a closer look at this company. And fast.

To track such startups, VC firm SignalFire has developed software that highlights companies on a dashboard when they start to grow rapidly or make notable strides. The platform tracks eight million startups globally, scraping data from sales records to scholarly publications and financial reports.

AI for diversity

Venture capital is still a male-dominated industry. In 2022, startups that have solo female founders attracted only 2% of the total VC funding in the U.S. Also, according to PitchBook, only 16.1% of industry decision-makers in the States are female. The situation for other underrepresented groups is not optimal either.

A few VC firms have already started using AI to ensure a more diverse approach to investing. EQT Ventures, for instance, has been integrating new features in its software Motherbrain to "dial-up underrepresented groups."

Overall, AI has the potential to assist VCs in making fairer, less biased investment decisions by reducing their reliance on gut feeling and warm introductions. And by 2025, more than 75% of VC investor reviews will be informed using AI and data analytics, according to consulting company Gartner .

Cheaper AI tools

Investors who are not ready to create their data-analysis platforms typically choose a combination of ready-to-use AI tools: ChatGPT, Google Bard (now Gemini), Copilot for market analysis, Tactiq for transcribing online calls, Vimcal for meetings scheduling, or Superhuman for email management.

Here’s a compilation of 79 services that investors use for deal flows, productivity and networking. Additionally, I recommend exploring this selection of 400 tech tools that venture capitalists add to their stack.

This article originally appeared on Entrepreneur , titled "How Venture Capitalists Are Using AI To Invest More Effectively." This is the edited version.

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