Venice: Building the Privacy Layer for AI

Introduction

AI is quickly becoming the default interface for work, research and decision making. As that happens, users are sending increasingly sensitive information through large language models operated by a handful of centralized providers.

Venice AI is built around a simple idea: users should not have to trade privacy for access to powerful AI. Rather than building another foundation model, Venice acts as a privacy layer between users and AI inference. Through a single application and API, it gives access to frontier, open source and Venice-specific models while allowing users to choose different levels of privacy depending on their needs.

In this report, we examine whether that thesis is beginning to play out. We look at why demand for private inference is increasing, how Venice differentiates itself from other AI platforms, whether onchain data points to genuine product adoption, and whether the token economics create a sustainable link between network usage and VVV value accrual.

Why Private AI Matters

The demand for private AI should continue increasing as LLMs become the default interface for work, research, finance and personal decision making.

The adoption curve is already clear. Data shows that 49% of Americans now use AI chatbots, up from 33% in 2024. As usage grows, so does the amount of sensitive information flowing through these systems. A 2025 study found that 82% of ChatGPT users considered their conversations sensitive or highly sensitive, with nearly half discussing health topics and over one third sharing financial information.