While typing out a sentence, AI can predict which word comes. It is surprising why the firms have not exploited the situation. AI’s power to predict is its biggest strength.
Plenty of data goes into training an AI-powered application or a tool used for recommendations like the Professional platforms like Linkedin, Crunchbase, Owler, and other networks. In addition, the economic research platforms driven by AI claim to have the ability to attract investors for a startup, and maybe there is a little truth too.
A study showed that the funds driven through AI created higher monthly returns over 15 years compared to human-guided competitors. Signalfire is a venture capitalist firm that uses Beacon software to perform investment target projection. It also uses the software to collect data sources for pitch decks, regulatory filings, academic publications, credit card data, and sales.
Signalfire is not the only company to employ AI in their jobs. Hone Capital is a leading firm in silicon valley that fabricated a model based on the data of 30,000 deals, considering if a team made it to the series of phenomenal conversion rates. While another firm that is biotech-based, Deep Knowledge Venture, says that they have inducted AI into their board of investors. The AI will be given a vote by the company’s financing, clinical trial data, rounds in previous funding, and intellectual property.
It is assumed that by 2025, 75% of the VCs will be using AI for suggestions on investment. With guidance on how to invest, where to invest, which team will succeed based on the history of employment and the success rate in the business previously run, they are most likely to succeed.
In an informal survey to understand the spread of AI in the VCs, some big firms like New Enterprise Associates, Accel, Index Ventures, Greylock, Plug And Play Ventures, etc., were spoken to, but they did not respond. This study suggests that AI is a closely guarded secret investment tool and is not the norm.
Raized.ai is a software vendor that selects the startup for VC based on decisions taken in the past and the current investment goals. The application of AI in VC ranges from tracing the companies that seek funds to tracking the growth rate of the company’s portfolio.
Despite all these factors, VCs do not seem ready to embrace AI. The reason may be simply because AI has not proven to be more efficient than the expertise the VC already has. An investment algorithm was built and compared with 255 angel investors in an experiment. The outcome was shocking as AI was biased. The algorithm picked only white people, and preference was given to startups with male founders. The reason may be that more white men represent the dataset.
An AI system needs balanced and adequate data, mainly when it is used to analyze investments. The Russia-Ukraine war and pandemic have disturbed the supply chains globally. Voleon, Winton Group, and Renaissance Technologies had considerable losses in 2020 due to COVID hitting the markets. However, the above findings are not suggestive that AI cannot apply to VCs. They can be used for the company’s parking lots satellite imagery that can give an idea of the crowd, merchandise sales, etc. The prior research shows AI can predict corporates’ decisions through linguistic signals.
Feng Li, an associate professor at the University of Michigan, stated, “the signs of self-serving attribution bias (SAB) — when people take credit for successes and blame others for failures — in the management discussion and analysis sections of annual reports tend to correlate with cash flow volatility. Firms with managers who had higher SAB performed worse than those whose managers presented as less egocentric.”
A combination of humans and AI seems to be the approach of most AI-powered firms. In addition, AI has the capacity for cost-cutting, which is a boon for investors as they spend 118 hours or more per startup. But it seems far away when AI will decide on investment.