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“A context graph is a living record of decision traces stitched across entities and time so precedent becomes searchable.”
— Jaya Gupta & Ashu Garg, Foundation Capital
“Data is no longer the new oil. It's decisions.”
— Foundation Capital, 'AI's Trillion-Dollar Opportunity'
“Context and memory may be the new moats for AI products.”
— Bessemer Venture Partners, State of AI 2025
Read the full analysis below
Deep dives into context graphs, decision intelligence, and the trillion-dollar opportunity.
The last generation of enterprise software created a trillion-dollar ecosystem by becoming systems of record — Salesforce for customers, Workday for employees, SAP for operations. The next trillion-dollar platforms won't be built by adding AI to existing data. They'll be built by capturing the decision traces that make data actionable.
When startups instrument the agent orchestration layer to emit a decision trace on every run, they get something enterprises almost never have today: a structured, replayable history of how context turned into action. The context graph compounds — the more workflows you mediate, the more traces you capture. The more traces you capture, the better you get at automating the next edge case.
Source: “AI's Trillion-Dollar Opportunity: Context Graphs” by Jaya Gupta & Ashu Garg, Foundation Capital (December 2025). Excerpts used for commentary and educational purposes with full attribution. All rights reserved by Foundation Capital.
Deep dives into how context graphs capture what systems of record miss — the reasoning connecting data to action.
Tracking the companies, funding rounds, and strategic moves shaping the trillion-dollar context opportunity.
Why retrieval-augmented generation was a waystation, not a destination — and what comes next for enterprise AI.
Approval chains happen outside systems — a VP approves a discount on a Zoom call or in a Slack DM. The opportunity record shows the final price but doesn't show who approved the deviation or why. This is what “never captured” means: the reasoning connecting data to action was never treated as data in the first place.
Context graphs change this. They extend knowledge graphs with temporal awareness, provenance scoring, and decision traces — answering not just “what” but “how” and “why.” Over time, exceptions become reusable knowledge rather than repeated human interventions.
Startups have a structural advantage here: they sit in the execution path and see the full context at decision time. Incumbents are either siloed or in the read path rather than the write path.
Analysis draws on research from Foundation Capital. All original content and analysis remains the intellectual property of its respective authors.
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