Measuring the costs, utilization, proficiency and impact of AI - with Russ Fradin, Founder and CEO, Larridin

**Ray Rike** (0:09)
AI to ROI podcast.
On today's episode, I am joined by Russell Fradin. He's the founder and CEO of Larridin. And we'll be covering four topics with Russell today. First, the AI visibility gap, the difference between AI deployments and AI adoption. And I'll add AI return on investment.

**Russell Fradin** (0:30)
Sure, that's all that really matters.

**Ray Rike** (0:32)
Exactly. Number two, measuring utilization versus proficiency versus impact, those are three different things.
Third, is the CFO and CIO accountability dynamic for AI investments. And then lastly, what AI to ROI will actually mean in the next 12 to 24 months. So with that, Russ, can you please take a moment to give a brief overview of your journey to becoming my guest here on the AI to ROI podcast?

**Russell Fradin** (1:00)
Sure.
Great to meet you. Thanks for having me. So I, you know, I moved to Silicon Valley 30 years ago and, you know, joined a startup, like, before Nescape went public, a long, long, long time ago. And I've been out in the Bay Area for the last 30 years doing a collection of startups. A couple, you know, early on in my career I was a very, very early employee, first employee kind of thing, at a couple of companies that became pretty successful. And then I founded a few companies that became, became large and were successful. And I started this company with a very, very long time collaborator of mine. He and I have worked together for the last 26 years. And we found a third co-founder who's been fantastic. And we started the company a little more than a year ago, raised $17 million from Andreessen Horowitz, from Gradient, which is the Google's AI fund, or it spins fun out, but at the time it was Google's AI fund, from Bloomberg, from Homebrew and Haystack, and a bunch of amazing angel investors. So, so far so good.
And you and I obviously met when I was speaking at the SASTER Summit within the last few weeks, all about AI measurement and what we're trying to build at Larridin.

**Ray Rike** (2:11)
Remember when cloud costs first started surprising people?
Finance would open the AWS invoice. No one could explain what drove it. And engineering would spend a week building a spreadsheet to explain it, and it was still wrong.
AI is doing that again, except faster, more dynamic, and spread across more systems and departments. Today, a single enterprise has multiple AI costs running across multiple vendors such as AWS, inference costs on Anthropic, and OpenAI, GitHub, Copilot, Cursor, and a handful of AI agents that no one billing system sees the entire spend picture.
So finance asked, what did we spend on AI this month? And the answer takes three days to find out, and it's still probably wrong. And what did we get for it? Most don't even try to answer that one. That is the problem Maverick was built for. Maverick gives finance, IT, and engineering a single source of truth for AI spend. So you can allocate costs, enforce budgets, and connect investments to business outcomes. Learn more at maverick.ai. That's M-A-V-V-R-I-KA-I.
Now on to the show. Yeah, and since we're on the AI to ROI podcast, it's perfect. And you said a couple names that really resonated with me because my first job in the valley was at a joint venture that GE did with Netscape. And then ultimately Netscape bought us where I led the e-commerce group. And Mark Andreessen and I were out there talking about internet, intranet and the extranet, which was-

**Russell Fradin** (3:38)
You know, he's went on to build some good things. I've heard of that guy.

**Ray Rike** (3:41)
Yeah, he's done a couple of good things, but hey, let's get into it. Cause when I was at Sasster, right? And let's call it what it was. It was AIster. It was all about AI and agents. And I must admit after going to two and a half days, I probably listened to 12 different sessions, I got frustrated. And the reason I got frustrated, Russell, was I heard so many operators talking about getting their employees to experiment with AI tools, get them to get used to building AI agents. But when I asked a question, even to the CMO of Snowflake about how are you measuring and managing the return on investment of these AI investments, there was very little about outcomes. So let me ask you, why aren't we even at least measuring the adoption, the utilization rate of AI, which hopefully will be a precursor to true return on investment?

**Russell Fradin** (4:33)
Well, look, companies are starting to and that's hopefully in part thanks to us. But normally, you and I, our careers are vaguely the same length of time in technology, which is a long time. Normally, when new tech comes along, even when it grows quickly, it doesn't grow this quickly. So there is an element where, I mean, generally what happens is large companies take a long time to adopt new technology. And part of it is they have planning and they have budgeting and they have all of these things that happen. And AI just hasn't been like that. So because it hasn't been like that, people are scrambling to catch up. So it's not like, is Snowflake, to use your example, it's not like the CIO of Snowflake doesn't know that he or she has to understand spend and budgeting and tracking and, of course, everyone knows these things. The issue is, you know, this is the first time we've had a, you know, technology deployed that went viral, wildly useful globally for every knowledge worker in every industry all around the world, right? It used to be something starts in Silicon Valley and then it takes a little while, it gets to New York and then maybe, you know, it gets to London and then it gets to the continent and then it gets to Japan and, you know, that used to take years, you know. Now you have people using Jet GPT in India and in Korea and in America and in the UK and in Germany and when you're in Berlin for work, it's no different than being in Boston these days in terms of AI adoption. It's really taken the world by storm. So if you have something that people know fundamentally, just know in their bones, it's going to matter, then of course, all of the normal measurement and tracking that people are used to will be a bit trailing. By the way, I saw this many, many, many, many, many years ago. I was probably among the first executives at a company called ComScore, before we had any customers. I guess I wasn't the first, I was the early executive.