Context Graphs - AI’s Trillion-Dollar Technology

**Ray Rike** (0:07)
Welcome to the first episode of the AI to ROI podcast, The Big Story. And I'm Ray Rike, founder and CEO of Benchmarkit.

**Peter Buchanan** (0:18)
And I'm Peter Buchanan. I'm the managing partner of New Plan.

**Ray Rike** (0:22)
And my co-author on the AI to ROI newsletter. And this week's Big Story episode is where we dive into the story of the week from the newsletter. But before we go into that, I just want everyone to know that we also have a second episode of the AI to ROI podcast every week. Well, I will be interviewing a guest who has recently deployed an AI project in their company. And they will be discussing how they justify the AI investment and then share the return on investment the project has delivered. Or at least the leading indicators they're using to be able to ultimately measure and report the ROI. I'll be hosting founders and CEOs, not only from enterprise companies who have deployed AI solutions, but also the founders and CEOs from AI native software companies to discuss their vision for the company in real life customer stories centered on the business impact and return on investment of deploying their solution in real life customers.
But let's go back to this episode, Peter. Can you introduce this week's big story that we'll be discussing? Sure.

**Peter Buchanan** (1:40)
We're discussing a relatively new technology that we think is incredibly important to the next generation of AI applications. They're called context graphs. And basically what they do is they fill in a gap in the AI stack in AI applications, infrastructure, applications, agents, all those things. And they provide basically connectivity so that when your applications are actually running, you not only see what happens, you see who decided to make those things happen, why they decided that, under what constraints they made those decisions, and what precedents they used to make those decisions. And so context graphs take what's happening and it creates an environment around it so that AI applications and AI infrastructure just run a lot better.

**Ray Rike** (2:39)
Okay, Peter, before we get into all those gory technical details, so this really came to light, I think it was sometime in middle December where two partners at Foundation Capital wrote an article on their blog. It ticked over LinkedIn. I have a hard time having a discussion with AI founders without it coming up. And in fact, Foundation Capital said, this is a trillion dollar opportunity. And even though I may question how big of an opportunity is, I cannot question how important context graphs are going to be to both validating and justifying the decisions that AI agents are going to make. And I just wanted to make sure I got it right.

**Peter Buchanan** (3:25)
Here's what I think about it, Ray, is it's not a trillion dollar opportunity in terms of, oh, they're going to spend a trillion dollars on this and it's going to dwarf spending on all other AI infrastructure and all other AI applications. And it's going to drive everything. But this technology is a glue technology that takes the promise of things like agentic AI and makes it much more effective. So we have an issue of AI to ROI coming out this week on error rate in models. And what that error rate actually means, well, this is a technology that can take the error rate in AI applications down to the level where you can almost always trust these applications. So when you look at a trillion dollar opportunity, what it is, is creating a trillion dollars of value for companies doing AI. So like when you read a McKinsey report, they never tell you the market size. They tell you the economic value that will be driven in the world by something that's happening. And in essence, that's what Foundation is saying, especially since there are no actual numbers in the report that gets you two trillion dollars. It's basically getting your attention saying, pay attention, this solves a big problem. And it creates a ton of opportunity for really enterprises of all sizes.

**Ray Rike** (4:54)
So Peter, so, you know, we kind of had this semantic web, which was introduced in 2001, and then it kind of evolved into knowledge graphs, which Google, I think, really we're talking a lot about starting in like 2010s. So our context graphs, just a natural evolution of the semantic web in knowledge graphs.

**Peter Buchanan** (5:17)
Yeah, it's like an injection of steroids, to be honest with you. So if you look at what a knowledge graph is, a knowledge graph will tell you customer X renewed a contract. The context graph will say, all right, well, who approved that renewal? What policies went into making that decision of approving that renewal? Maybe you'll know that on the customer side and your own side.