Episode 6
Can a context graph alone
make AI reliable?
Late last year, Jaya Gupta called the context graph AI's next trillion-dollar opportunity: the reasoning behind a company's decisions that systems of record never stored. The post set off a wave of debate across the field. What it left unsettled is whether a strong graph is enough alone, or whether reliable AI needs the context layer around it, the wider system that governs how those decisions get used. Jaya joins Austin to work through what a context graph must hold and where it stops.
Questions we'll tackle:
- How much of the reasoning behind a decision can actually be captured as data?
- How does a context graph stay accurate when the enterprise changes?
- What happens when an agent faces a decision the context graph has never seen?
- What keeps two agents using the same context graph from contradicting each other?
SPEAKERS
CONVERSATION LEADERS
HOST
Director of Data Strategy, Atlan
Former Gartner Director who spent years advising Fortune 500 data leaders on analytics strategy — now Director of Data Strategy at Atlan. He's the person large enterprises call when they can't figure out why their AI keeps getting context wrong.
GUEST
Partner, Foundation Capital
Partner at Foundation Capital. Late last year, she and Ashu Garg published "AI's trillion-dollar opportunity: Context graphs," an essay arguing that the reasoning behind enterprise decisions — the part no system of record captures — is the next major infrastructure layer for AI. The piece set off a wave of debate across the field. She joins Austin to work through what a context graph actually has to hold, and where its job ends.