What if AI can learn the “why” behind decision making of humans?

Ashu Garg and Jaya Gupta recently wrote one of the most discussed articles on AI this year. Their idea drew public responses from Dharmesh Shah, Aaron Levie, and Arvind Jain.

Enterprise software has always captured what happened. It records the order, the ticket, and the approval. But it has never captured why it happened. It does not store the reasoning, the exception, or the past decisions that shaped the outcome. Ashu argues that this missing layer is the biggest opportunity in enterprise AI right now, and that the startups that capture it will be the biggest winners in AI.

In this episode, we go deeper into what context graphs really are, how they get built, why startups have an edge over incumbents, and how close we are to seeing this work in practice.

00:00 – Trailer
00:32 – What are context graphs?
02:48 – Why agents haven't lived up to the hype?
05:18 – The "why" of Decision Making
08:17 – How agents will store data for context graphs
10:38 – What will be possible for Digital twins?
14:02 – Can context graphs reveal a company's moat?
15:50 – Guardrails on Access for agents
19:50 – Managing agents vs being managed by agents
23:01 – Will winners be vertical or horizontal players?
25:56 – The future is agent swarms
28:43 – Finding PMF is what makes a great CEO
31:40 – What will set apart successful enterprises of 2030
33:44 – Where Foundation Capital is investing
35:16 – Why AI won't be winner-takes-all
37:38 – Where will the context graph reside?
40:45 – Will systems of record be replaced?
42:42 – Human in the loop → hands-off execution
44:46 – A reality check on where we are today
46:43 – Where startups will win in orchestration

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This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.

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