- Venture capital funds are betting on “context graph” as the next wave of AI, focusing on storing the reasoning behind decisions rather than just outcome data.
- For 40 years, enterprise software has only recorded “what happened” without saving “why it happened,” causing critical knowledge to be lost when personnel leave.
- Context graph connects “decision traces” such as who approved, which exceptions applied, and what precedents existed, creating a system where decision logic can be queried.
- The market opportunity is not just the $200 billion SaaS market, but up to $4.6 trillion spent on salaries and services—where human decisions have not yet been digitized.
- AI agents are driving this trend because they generate and need to store decision chains when operating across multiple systems.
- Startups have an advantage because they sit directly in the execution flow, where they can record decisions as they happen, rather than just reading data after the fact.
- For example, the open-source gstack reached nearly 20,000 GitHub stars and over 2,200 forks in just a few weeks, showing strong demand for agent infrastructure.
- However, there is no clear leader yet, and significant challenges remain regarding security, access rights, and reasoning over sensitive data.
- Giants like Salesforce, Workday, and ServiceNow are entering the space but are limited by legacy architectures that only store the current state.
Conclusion: Context graph is seen as the new infrastructure layer for enterprise AI, capable of turning fragmented decisions into accumulated “organizational intelligence.” With a potential scale of $4.6 trillion—far larger than the $200 billion SaaS market—this could be the next major leap for AI. However, the market lacks a dominant leader and must solve problems regarding security, architecture, and trust before it can truly explode.
