News - May, 2023

Are Artificial Intelligence and Machine Learning set to revolutionize the PE Industry?

ChatGPT has made the power of Artificial Intelligence ("AI") and Machine Learning ("ML") both visible and easily accessible. Every industry needs to figure out how to leverage these capabilities. Private equity is no exception.

 

AI/ML is already being used across the PE/VC value chain

While AI/ML and data-driven investment models are relatively new to the private equity and venture capital industries, they are well established in hedge funds, where almost all firms rely on quantitative trading strategies.
A similar quantitative revolution to the one that changed the hedge fund industry in the early 1990s is about to hit the PE/VC world. Two factors are driving this: 1) Good data is becoming more and more available, driven by open platforms such as LinkedIN, Github, web crawlers and other sources, as well as paid data providers such as Pitchbook or Crunchbase. 2) AI/ML technology is advancing in leaps and bounces and enables the effective interpretation of ever more data sets.

Applications range across the entire value chain of the PE/VC business:

• Origination: Best established is the idea of scouting the market of investment opportunities using technology. AI may not tell you where to invest, but it can tell you who to talk to. Some funds track thousands of early signals, such as HR movements, Github, blogs, app-store and web traffic, regulatory filings and many, many more to identify rising stars
• Selection: Other funds have pushed the concept further, They are not only identifying opportunities using technology, they are also relying on data for the investment decision. EQT has built technology called SiRE that can forecast revenue for growth businesses – a key valuation input. Correlation Ventures has built an entire business model around making investment decisions within days, largely based on data and technology 
• Talent Management: Backing the right management teams and enhancing teams with the right hires is another key factor in successful PE operations. AI increasingly helps with HR decisions. Firms such as Signalfire have developed talent platforms for themselves and their portfolio. Entrepreneur First has built software for identifying, bringing together and backing founders at pre-idea stage
• Fund Operations: The grind work of fund operations has already been made more efficient using digital platforms. FF Venture Capital created the Totem platform for its fund operation. It proved interesting enough to be spun out as a business. The market is currently seeing a wave of new entrants which could be seen as a proof point for the vast potential in this space. 
• Generic productivity tools: Last but not least many general purpose productivity tools such as ChatGPT itself, Compose.ai for writing, Calendly for scheduling or Ontra and Henchman for legal work are also being used. Mainstream business software developers such as Notion.ai, Salesforce or Microsoft have started to incorporate AI features in all products.

 

Hybrid deployments emphasizing man-machine collaboration have proven most successful

Today, the industry is still in a state of "capability overhang"-that is, enormous capabilities exist, but the best use and proper deployment of these technologies is still unclear. However, some early lessons have emerged:

• Hybrid deployments that balance quantitative inputs (e.g., performance metrics) with qualitative inputs (e.g., strategy or team assessment) have proven most effective.) Great systems are not enough; you also need great investors and experts, and the combination of the two is optimal.
• Strong collaboration capabilities improve engagement and allow a group of people, such as the investment team and external consultants, to look at the same data and comment on and improve it. Human and digital experts collaborate around the same data. This leads to better results and, in the long run, new data for subsequent analysis.

 

Build vs Buy

PE/VC funds looking to deploy AI/ML technologies today are facing a real dilemma. On the one hand, there are only few off-the-shelf solutions such as Affinity, Synaptic or Raized.ai and they certainly do not cover all the needs and possibilities.

On the other hand, building your own AI/ML technology stack is not for the faint of heart. Signalfire claims to spend US$10 million annually on its technology. InReach Ventures says its initial technology investment was US$3.5 million. EQT’s “Motherbrain” team easily numbers 30 highly qualified experts.  The average fund engaging in the activity has €500 million AUM and employs three engineers full-time.

Funds will need to decide whether a fast follower strategy using established third-party products is enough or whether they can achieve real differentiation by building bespoke solutions at considerable cost.

 

Conclusion

Artificial Intelligence and Machine Learning will undoubtedly revolutionize the PE industry. Gartner predicts that AI will be involved in 75% of venture capital investment decisions by 2025. Every player in the industry will need to find their technology stack, using the right mix of off-the-shelf or bespoke solutions.

However, over 5-10 years the changes are likely to be more profound. A virtuous cycle of more data leading to better outcomes and then more data being used will change the shape of the industry. Leading funds will be able to rely on a powerful technology stack that allows them to deploy capital in larger geographies, more quickly and with better decisions quality almost as easily as in the hedge fund industry. The leaders of tomorrow will be large, global technology players, not dissimilator to today’s hedge fund giants.

On the other hand, players that do not acquire these capabilities will not have the same geographic reach, investment pace and quality of decision making. They may continue to operate in small niches, or, one day in the future, no longer be able to compete for capital either.

 

by Roland Dennert, Managing Partner at Cipio Partners