Skip to main content
About HEC About HEC
Summer School Summer School
Faculty & Research Faculty & Research
Master’s programs Master’s programs
Bachelor Programs Bachelor Programs
MBA Programs MBA Programs
PhD Program PhD Program
Executive Education Executive Education
HEC Online HEC Online
About HEC
Overview Overview
Who
We Are
Who
We Are
Egalité des chances Egalité des chances
HEC Talents HEC Talents
International International
Campus
Life
Campus
Life
Sustainability Sustainability
Diversity
& Inclusion
Diversity
& Inclusion
Stories Stories
The HEC
Foundation
The HEC
Foundation
Summer School
Youth Programs Youth Programs
Summer programs Summer programs
Online Programs Online Programs
Faculty & Research
Overview Overview
Faculty Directory Faculty Directory
Departments Departments
Centers Centers
Chairs Chairs
Grants Grants
Knowledge@HEC Knowledge@HEC
Master’s programs
Master in
Management
Master in
Management
Master's
Programs
Master's
Programs
Double Degree
Programs
Double Degree
Programs
Bachelor
Programs
Bachelor
Programs
Summer
Programs
Summer
Programs
Exchange
students
Exchange
students
Student
Life
Student
Life
Our
Difference
Our
Difference
Bachelor Programs
Overview Overview
Course content Course content
Admissions Admissions
Fees and Financing Fees and Financing
MBA Programs
MBA MBA
Executive MBA Executive MBA
TRIUM EMBA TRIUM EMBA
PhD Program
Overview Overview
HEC Difference HEC Difference
Program details Program details
Research areas Research areas
HEC Community HEC Community
Placement Placement
Job Market Job Market
Admissions Admissions
Financing Financing
Executive Education
Home Home
About us About us
Management topics Management topics
Open Programs Open Programs
Custom Programs Custom Programs
Events/News Events/News
Contacts Contacts
HEC Online
Overview Overview
Degree Program Degree Program
Executive certificates Executive certificates
MOOCs MOOCs
Summer Programs Summer Programs
Youth programs Youth programs
Instant

How AI is Affecting VC Funding of Innovative Startups

Finance
Published on:

Artificial Intelligence adoption by investors might hinder the allocation of capital to breakthrough innovations. Learn more in this interview with Maxime Bonelli, PhD student in Finance at HEC Paris, on his dissertation. Maxime focuses on the real effects of new technologies and human capital in the financial sector, to help us better understand how the industrial organization of the financial sector affects the real economy.

man_and_woman_looking_at_algorithms_cover

Photo Credit: deagreez on Adobe Stock

You’ve been studying the adoption of AI by venture capitalists. Can you give us some background on your decision to investigate this topic?

In my research paper selected for the job market, I study how the adoption of artificial intelligence by venture capitalists (VCs) to screen startups affects the funding of early-stage innovative companies. The motivation relates to the fact that the past two decades have witnessed rapid growth in data availability and processing thanks to statistical techniques such as machine learning and AI. The adoption of these technologies by financial intermediaries has however raised concerns regarding their effects on investment decisions and, more broadly, on the allocation of capital. In my research paper, I focus on a key class of financial intermediaries— VCs, which are private equity investors in startups with high growth potential and play a crucial role in the financing of innovation.

Can you explain how venture capitalists use AI tools?

In recent years, dozens of VCs have adopted AI technologies for screening startups, i.e., sourcing, evaluating and selecting startups to fund. These VCs have developed their own proprietary platform which automatically tracks and scores startups in terms of future return prospects. Put simply these VCs employ algorithms to detect quantitative patterns in historical data from previous startups and extrapolate them to predict a new startup’s outcome. 

How do you identify the venture capitalists who use AI?

I develop a classification of VCs to determine whether and when they adopt AI. Using job and employee data, I identify VC firms that hire data scientists who develop machine learning algorithms for investment screening, and I call these employees AI-related employees. Using job starting dates, I classify a VC as becoming AI-empowered from the date it hires one AI-related employee. 

So, how is the use of AI by VCs changing their funding strategy?

In a nutshell, I show that VCs that adopt AI become better at identifying good quality startups, i.e., those that survive and receive follow-on funding, but only within the pool of startups whose business is similar to that developed by past companies. At the same time, VCs that adopt AI become less likely to fund breakthrough companies, i.e., startups that achieve an IPO or obtain highly cited patents. This finding is associated with an increase in the share of their investments being oriented toward startups developing businesses closer to those already tested. These results are consistent with AI exploiting past data informative about companies similar to past ones but not informative about breakthrough companies. Overall, my paper shows that AI adoption by investors might hinder the allocation of capital to breakthrough innovations.

 

VCs that adopt AI become better at identifying good quality startups, but only within the pool of startups whose business is similar to that developed by past companies.

 

How do you define and identify innovative startups vs. startups similar to past ones?

I construct a measure of “backward-similarity”. Specifically, I measure the similarity of the text we find in a startup’s business description and compare it to those of previous VC-funded startups in the same industry. So high backward-similarity startups run businesses similar to those that have been already tested by past startups. In contrast, low backward-similarity startups are more likely to be innovative and to develop novel products.

A few words on the method now: how can you be sure that your results are causation and not correlation?

To provide causal evidence that AI adoption leads to changes in VCs’ investments, I use a plausibly exogenous shock to one often cited determinant of a VC’s decision to adopt AI: the number of potential investment opportunities it faces. Indeed, given the large, fixed costs of evaluating investments and the limited scalability of VC firms, more investment opportunities make screening more onerous, creating incentives for VCs to adopt AI technology to automate screening with a view to saving time and costs. Specifically, my empirical strategy uses a quasi-natural experiment: the introduction of Amazon Web Services (AWS), i.e., cloud computing services by Amazon. This shock lowered the cost of starting new software- and web-related businesses, leading to more startup creations in specific industries and thus an increase in investment opportunities faced by VCs. 

What do you think are the big implications of AI adoption for the VC industry and the funding of innovations?

Taken together, my results show that AI adoption by VCs affects how they select their investments and, more broadly, how capital is allocated among young innovative companies. This suggests that AI adoption by investors can shape the nature of innovation and thus can have a significant impact on future growth trajectory.

Answers by Maxime Bonelli based on his PhD dissertation, “The Adoption of Artificial Intelligence by Venture Capitalists” conducted at HEC Paris. Maxime Bonelli has received the best paper award 2022 for his dissertation, from the European Finance Association (EFA) at their Doctoral Tutorial (DT), a one-day, competitive session designed for PhD students in Finance. Learn more on that award here.
Subscribe button for Knowledhe@HEC newsletter

Newsletter knowledge

A monthly brief in your email box and 3 issues of the book per year.

follow us

Insights @HECParis School of #Management

Follow Us

Support Research

Our articles are produced thanks to our reader's support