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Deal sourcing refers to the processes by which venture firms identify potential investment opportunities. Some may argue that generating high-quality deal flow is one of the top indicators of future returns and the overall health of the fund. Therefore, it is a responsibility that requires the participation of every member of the fund, including entry-level analysts, associates, and partners. A survey run by Harvard Business Review between the years of 2015 and 2016 found that “On average, they (VCs) put 55 hours a week in on the job, spending 22 hours a week networking and sourcing deals and 18 hours working with portfolio companies.”
Investors source deals in 3 main ways:
All of this being said, networks continue to be the foundation of accessing quality deals and capital. This creates several barriers for talented entrepreneurs who are not connected to high-profile individuals through their university, city, past employers, etc. In turn, it becomes very easy for investors to exist within a bubble where they continue to interact with (and fund) individuals who are similar to them in terms of gender, race, and socioeconomic background.
In 2017, Social Capital launched a machine learning tool known as Capital-as-a-Service (CaaS) to democratize access to entrepreneurship. It was clear that the fundraising process, which heavily depended on warm intros, continued to promote bias in the system. The solution was to allow anyone from anywhere in the world to apply for funding through a uniform process. There would be a standardized data request with no partner meetings and pitch decks required. The decision to invest would be entirely machine-led with quick turnarounds and actionable feedback.
In its pilot, Social Capital is said to have surveyed 3,000 companies, invested in several dozen across 12 countries with 42% having female CEOs and the majority being non-white. From afar, the tool was working and Social Capital was able to identify promising startups from all over the world without the bias of human judgment. However, founder Chamath Palihapitiya has stated that the administrative burden became inconceivable (source). In 2019 the firm upended its strategy and shifted toward making fewer but larger investments.
1. Buy. Using pre-existing AI tools can be a cost-effective and user-friendly way to incorporate AI into deal sourcing. However, it's important to consider that there is a risk of accessing the same deals as other clients and limited room for customization.
2. Build. Some funds may opt for developing an in-house platform such as EQT's MotherBrain or Connetic Ventures' Wendal. Nevertheless, this usually entails a substantial investment in hiring a tech team to construct and oversee the tool.
According to research firm, Gartner, Inc., more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics by 2025.
VC Stack hosts several Deal Sourcing tools. We’ve included a list of a few AI-powered platforms to check out.
One of the recurring points about AI integration is that it should be used in conjunction with human decision-making. Incorporating AI tools into deal sourcing should not be limited to relying solely on metrics without considering factors such as fund capacity and management logistics. Especially in early-stage investments, bets are placed more heavily on the founders and so setting up meetings and gaging interpersonal skills continue to be valuable. In the survey previously mentioned by Harvard Business Review, 95% of VC firms cited founders as an important factor in their decisions to pursue deals. Therefore while AI can allow funds to efficiently and effectively sort through data, more than an individual could ever do alone, it is not able to outsource intuition.
Maxime Bonelli, a Ph.D. candidate in Finance at HEC Paris published a dissertation on The Adoption of Artificial Intelligence by Venture Capitalists. While concerns around AI tools for deal sourcing are few, Bonelli’s research highlights an interesting effect it may have on funding innovative companies.
Using global data on VC investments, I show that after adopting AI, VCs tilt their portfolios towards startups whose business is similar to those already tested by past startups. Within this pool of startups, AI-empowered VCs become better at picking those that survive and receive follow-on funding. At the same time, these VCs' investments become 18% less likely to result in breakthrough success. I exploit plausibly exogenous variation in VCs' incentives to automate screening from the introduction of Amazon’s Web Services to establish causality between AI adoption and the above effects. Overall, my results are consistent with AI exploiting past data that are not informative about breakthrough companies. AI adoption by investors may therefore reduce the capital directed toward innovation.
We invest early at the seed stage in leaders leveraging data and technology solutions to upend substantial markets. We developed a first-of-its-kind centralized infrastructure implemented across the firm.
We apply a rigorous data-driven approach to identify those start-ups with the greatest potential for superior returns. We invest in seed stage and also selectively in high-quality growth/later-stage deals.
We are a growth-stage investment firm located in San Francisco. The firm is best known for its use of quantitative decision principles such as stochastic finance and multi-factor analysis in venture capital.
We leverage world-class analytics to offer entrepreneurs and other venture capitalists a dramatically better option when they are seeking additional capital to complete a financing round.
We use proprietary software (known as DIG) to discover, evaluate and support investments in the most promising startups. InReach invests in early-stage technology companies all across Europe.
We invest in all development and growth phases of technology companies. Earlybird offers its portfolio companies financial resources, strategic support, and access to an international network and capital markets.
Interested in learning more about how AI is impacting VC? Check out these additional readings!