How to Become a VC in the Age of AI — Apply to the PreSeries FutureVC Program

PreSeries is a Machine-Learning-as-a-Service (MLaaS) platform that collects startup data from a vast set of sources to generate the most accurate picture of early-stage companies and their industries. Startup investors use PreSeries to automate their startup deal-sourcing and assessment efforts. We help VC firms become software companies. Get in touch.
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If you are reading this, you want to become a venture capital investor (or learn how to effectively be one on the age of AI).

There’s abundant literature on how to work your way to success in venture capital. Some lessons from the past still apply, so we strongly recommend you to read from like Brad Feld, Mark Suster, Albert Wenger, Fred Wilson or Benedict Evans to name few of the greats.

But for you that are about to start your VC journey, getting familiar with the latest technologies reshaping the industry is a no-brainer. It’s the only way to differentiate yourself from your peers in this highly competitive environment. We designed this program to generate awareness among tomorrow’s startup investors. Traditional venture capital is changing forever, but mastering predictive technologies is the surefire way to stay ahead of the pack.


It was only few years ago that a handful of VCs started to experiment with automation and machine learning as part of their internal operations. Today, you see VC firms publishing offers for data science jobs and openly talking about the different ways they use machine learning.

Venture Firms are in need for new venture investors that also understand how to apply new technology to improve the investment process. Not only that, we also believe the industry will be transformed radically in the next few years. We also believe that in next generation of venture investors, those understanding AI will have the best shot and will be preferred by founders and LPs.

Even if the industry as a whole is still lagging behind, recent announcements from big funds pursuing AI-related initiatives are sparking the interest of more traditional startup investors. In addition to internal projects from VC firms, providers of AI solutions for VCs also start to emerge and offer a quicker, more robust path for investors to adopt machine learning.

The VC world is now at crossroads. Relying on data and predictive technologies, is choosing the path less traveled. For many it’s a leap of faith. It’s usually safer to stick with what you know, aka the gut-feel approach that has proven right times and times again. That is why we are announcing our brand new tutoring program to help anyone from the startup investing community, especially motivated students, to learn how to apply machine learning to startup data. We want to demystify how predictive technologies work with concrete step-by-step examples on how to apply it to your OWN data.


PreSeries’ FutureVC program

What we offer is very simple, and honestly we think it’s your best strategy to really impress venture partners at your future job interviews.

We encourage you to create your own portfolio of at least 100 startups. We will help you on the data collection phase. We are providing you with a data collection template to get started... It will serve as your proprietary portfolio. It will remain private and be used as a dataset to kickstart your machine learning journey, and especially how relying on predictive models improves how you can manage your dealflow. I invite you to read more about the GASP open-source framework to learn how to collect startup data more easily.

The first ever framework to generate machine learning ready datasets for startup data.
Download your own open-source GASP for the FutureVC program

The FutureVC program is 100% free tutoring program, no strings attached, and you keep complete ownership of your data and models. If the conditions for a successful tutoring are met, we’ll schedule three 30 minutes video calls where we will run you through the whole machine learning process, tailored with your data:

  • Which modeling approach to choose
  • How to do feature engineering
  • How to evaluate results
  • How to integrate predictive models in your workflow

In the tutoring sessions, we’ll show you how to create predictive models that answer questions such as:

  • Should I spend time interviewing this startup?
  • How likely are they to raise a Series A?
  • How can I score and rank my deals to spend more time on the most promising ones?
  • and more …

You have the data? Great! Get in touch with us at futurevc(at)preseries(dot)com and we’ll schedule a discovery call with you. Everything will take place on the PreSeries Analyst Platform, no prior knowledge of machine learning is required. You’ll be given access free access to the platform for the duration of the tutoring. Feel free to apply as a group or by yourself.

Why you need to bring your own data

The main reason is that there is no standard approach to apply machine learning to venture capital. Every problem is different and deserves a custom solution. With a sample of data we can define a solution that works for you.

When it comes to startup data, the trade-off always remains data quality vs. data quantity. For example, widely available data in public databases, such as Crunchbase, Pitchbook, Owler, or Dealroom, are in the quantity game. Information is abundant but rarely go into details when dealing with small companies. Data is scarce, sometimes vague and often outdated. It makes for great industry level analysis but not so much at the company-level. Who can blame them? Data collection at this level has to be done manually in most cases. Some players like CB Insights realized it could automate some parts in its data collection process (they claim 70%).

There are no shortcuts to achieve superior data quality. By 2012, Correlation Ventures had already partnered with 20 VCs to access their internal statistics and reached to hundreds of companies manually. They gathered a dataset of 80,000 equity financings in which at least a VC firm participated since 1987. These sources are now benchmarked against the internal data available for each company applying for funding. All applicants are required to submit basic planning, financial history, and legal documents (e.g. term sheets, cap tables). The data is then used in the firm’s analytic models. Their sustained efforts have translated in one of the most automated processes in the industry: Once a start-up scores high based on their criteria, only a single 30-minutes interview in person is needed to make a decision. It reduces the time required for decsion making to an average of 2 weeks.

With our tutoring sessions you will learn how to apply the industry’s best practices when it comes to machine learning and you’ll have the tools ready to replicate what the top VC firms jealously keep for themselves.


Have data on at least 100 startups? Get your free tutoring sessions now at futurevc(at)preseries(dot)com