On the Future of VC, Machine Learning and Your Startup
Updated: Apr 4, 2019
I sat down recently with Prasant Sudhakaran, Co-Founder and Director of Corporate Development at Aingel, a platform used to predict the success of entrepreneurial teams by analyzing over a 100 factors, including work history, educational details, prior startup and fundraising experience, and the digital footprint of individual members of the team. Prasant has also recently served as an adjunct professor at NYU and an esteemed mentor in the NUMA New York network. Over the course of the past several months Prasant has assisted NUMA in its selection process, evaluating the pitches of our potential companies and using the Aingel platform to analyze the potential of our founders. In our conversation, Prasant shared context on Aingel’s inception, and took a deep dive into the history and trends of the VC industry.
Grad School to Boardroom — Aingel.ai
Aingel began as a grad school project of its 3 co-founders, researching the ability of machine learning to predict the success of teams. Turning the complex research done in this graduate school setting into a successful company wasn’t easy, however, over the course of the past two and a half years Prasant and his team have reached increasing levels of growth. Expanding beyond its initial offering to analyze the success of a startup based on its team, Aingel now leverages its proprietary technology to facilitate VC/startup partnerships!
To Data or not to Data
When asked about the trends in the VC industry, Prasant pointed to the gradual acceptance and embrace of data within the industry. When Aingel first began analyzing the likelihood of companies’ success, many in the VC world met the offering with skepticism, asking “Are you going to automate my job away?” or “How can you predict this with your data?” Over time, Prasant has seen the VC industry become more accepting of data-driven methods, gleaning relevant insights on entrepreneurial success and VC partnerships as a result of the Aingel platform.
Will VC Survive Automation?
Today, some in the entrepreneurial community may be wary of the long-term sustainability of VC as a profession, especially as data continues to enhance our decision making. Prasant points to the value of VC support that goes beyond access to capital. Machine Learning may help us most-efficiently allocate capital to entrepreneurial teams, however, early-stage VC investors also provide expertise, guidance and network beyond capital that are also crucial to the success (or failure) of a young company. As our reliance on data to help us make decisions increases, Prasant is confident that VCs will continue to operate as valued connectors and advisors for aspiring entrepreneurs.
Why Does Prasant Mentor at NUMA?
After being helped by many people who have expected nothing in return, Prasant is happy to give back as he can.
Thanks Prasant, NUMA deeply appreciates all the help and time you have shared with the community!
Prasant is the co-founder of Aingel.
Prior to co-founding Aingel, Prasant spent over 10 years in finance and consulting for firms across geographies. Starting his career as a fixed income trader, he moved on to management consulting, where he worked with multiple Fortune 500 companies, SMBs and not-for-profit organizations.
Prasant’s interests lie in using Machine Learning and Artificial Intelligence in the areas of finance and marketing. He has a BA in Economics and Finance from De Montfort University, and an MS in Business Analytics from New York University.
Maor is the Program Manager at NUMA New York.
Maor supports program operations and outcomes alongside NUMA’s Program and Managing Director. At NUMA, Maor has ensured cohort startups have immediate access to all program assets, as well as a smooth experience throughout the program.
Prior to working at NUMA, Maor consulted for Israeli startups on US market-entry and how to obtain funding domestically as well as abroad in the US.