VC AI Funding
1 Introduction
Explain why you chose this topic, and the questions you are interested in studying. Provide context for readers who are not familiar with the topic.
(suggested length: one paragraph)
1 Introduction
We chose this topic because we are interested in venture capital funding, particularly how it has evolved in the age of AI. We’ve constantly read news articles about new AI companies being given millions, if not billions, with no real product, which has piqued our interest in visualizing how VC funding works and how it has changed in this new age of AI.
A few questions/subtopics we were interested in:
- Which industries do VCs invest in, and the different variables that affect the amount of money they give (startup location, founder’s alma mater, etc.)?
- How has the average deal size or funding frequency changed from pre-2020 to post-ChatGPT (2022–2025)?
- Are venture capitalists prioritizing AI as a buzzword, or are they investing in companies with measurable technological depth?
- Which investors or venture funds are most active in AI-related deals, and how concentrated is the funding landscape?
- How has the distribution of venture capital funding across industries shifted since the rise of generative AI?
2 Data
Identify one or more data sources that you propose to draw on for the project. For each, describe how the data are collected and by whom. Describe the format of the data, the frequency of updates, dimensions, and any other relevant information. Note any issues / problems with the data, either known or that you discover. Carefully document your sources with links to the precise data sources that you used. If that is not possible, for example, if your data is not available online, then explain that clearly.
(suggested length: one paragraph)
For this project, we plan to use data from PitchBook, a financial data and research platform that tracks private and public market activity. The data on PitchBook are collected directly by the company through a combination of public filings (like SEC reports), press releases, company websites, and interviews conducted by in-house research analysts. PitchBook also receives information submitted directly by firms, investors, and startups through verified accounts.
The data are delivered in a structured format, typically as downloadable CSV or Excel files, or accessible through their web interface and API. The platform is updated on a daily basis, especially for major transactions like funding rounds, mergers and acquisitions, or valuation updates. Each entry usually includes multiple dimensions, such as company identifiers, industry codes, investor names, deal types, deal sizes, valuation metrics, and key dates.
One issue with using PitchBook is that some data are proprietary or incomplete, especially for private companies that don’t disclose full financial details. In addition, access requires an institutional license, so we may be limited to the datasets available through our university’s account.
Optional:
Include a few initial graphs