Here’s How AI Is Disrupting Private Equity & Venture Capital

To Investors,

The other day I came across a post from Steven Bartlett about how there isn’t enough of a discussion about AI agents and how they are disrupting every industry. I wanted to discuss how these agents apply to the fields that I’m involved in, and even though I’ve tracked a lot of this activity for years, I’m continuously mind-blown at the information that I come across.

What are AI agents?

AI agents are software systems that perform tasks or make decisions by processing information and acting toward specific goals. Advanced AI agents can reason, handle complex tasks, and sometimes collaborate with humans. The best ones improve over time by learning from new data, algorithms, or experiences — much like how you and I refine our decisions with more knowledge and intuition.

According to a March 2025 report from Datagrid, adoption of AI agents among fortune 500 companies has grown 450% since 2022, and these agents are improving diagnostics in healthcare; assisting with inventory management in retail; they’re reducing costs and improving operational efficiency in manufacturing; they’re being used for precision farming; and obviously we’ve all seen the self-driving capabilities that Tesla and Waymo are unlocking. There are way more applications that we can discuss, but today I want to talk about how AI agents and AI more broadly is disrupting the private capital markets (i.e. venture capital and private equity).

I’ve approached this thought two ways:

* AI for Internal Operations in a VC/PE firm.

* AI as part of the investment decision-making criteria.

Internal Operations

Traditional venture capital and private equity involves investing in companies with high growth potential, typically in exchange for equity, with VC/PE investment firms playing an active role in guiding these companies while in the early stages. In the later stages of a company lifecycle, capital allocators may play a role in helping the companies transform their operations to improve their capacity and scale. An AI system replacing this model would need to replicate or enhance four key functions:

1. Deal Sourcing:

* Purpose: Identify promising startups.

* How It Works: The AI scans vast datasets to find opportunities, analysing industries, team backgrounds, early traction, and market trends.

* Example Features: Real-time alerts for emerging startups or ranking systems based on growth potential.

2. Due Diligence:

* Purpose: Evaluate the business’s potential and risks.

* How It Works: The AI processes business plans, financials, and market data to assess viability and scalability.

* Example Features: Automated risk scores, financial health projections, team competency analysis.

3. Investment Decision:

* Purpose: Decide which businesses to fund.

* How It Works: The AI uses predictive models to score startups against your firm’s investment criteria, recommending the best fits.

* Example Features: Success probability forecasts or portfolio fit analysis.

4. Portfolio Management:

* Purpose: Monitor and support invested companies.

* How It Works: The AI tracks performance metrics and provides insights or recommendations for growth or exits.

* Example Features: KPI dashboards, exit timing predictions.

An internal operations AI model would be data-driven, scalable, and capable of reducing human bias while speeding up processes traditionally reliant on manual efforts.

Let’s get practical. You’re probably wondering if there are any VC/PE investment firms that are actually using AI agents in their internal operations. The answer is yes, and many have been doing so for years…

EQT Ventures (Motherbrain)

EQT Ventures developed Motherbrain in 2016, an AI platform that