Token Economics, AI ROI, and the Future of Investment Banking with Rahul Rekhi

**Rahul Rekhi** (0:00)
I love to think in terms of movie metaphors, and one of my favorite metaphors comes from Interstellar. There's a bit at the end with Matthew McConaughey, and he's working with entities that can time travel, and they don't know what to do with that immense power. And they work with Matthew McConaughey's character because he has a relationship, he has an empathy for the person they're trying to contact, and can take their immense power and channel it to something that actually drives change, that actually delivers a message. And I think there's an analog on that with AI, which is part of both the promise but the parallel of these AI tools, especially as the models continue to get smarter, is they can do just about anything. Software tools could do a very specific set of things and you could earn that. The challenge and opportunity with AI is because they can do everything, you need to be guided in how to actually use them to be useful to you.

**CJ Gustafson** (0:48)
Is this thing on?

**Rahul Rekhi** (0:52)
Yesterday's price is not today's price.

**CJ Gustafson** (1:03)
Welcome back to Run the Numbers. I've got an exciting one for you at the intersection of finance, operating a hyper growth company in investment banking. This episode is with none other than Rahul Rekhi, the president of Rogo. So if you haven't heard of Rogo, they are vertical software that is AI native for investment bankers and financial professionals. So if you've ever had to create one of these pitch decks as an investment banker, you know how tedious they can be. They take all the workflows that investment bankers go through to create their collateral and to evaluate deals, and they overlay an AI native platform so you can get it done a lot faster. This company is growing at a crazy, crazy rate, and he's the president there. So he's responsible for finance and much of their operations. We go deep on token economics. Yes, we talk about token maxing, and more importantly, ROI maxing. This guy knows his stuff in terms of what you need to measure across the different models you deploy.
When you are the seller passing through much of these AI costs. So if you're sitting there on your finance team saying, how do we actually gauge ROI on the tokens that we were spending? He makes it pretty clear for you. We also talk about this forward deployed banker model, and how they look at repurposing the knowledge that many of these bankers have to both sell and deploy their software at scale, which is pretty neat. We talk about these go-to-market engineers, something that Clay made famous. We talk about forward deployed engineers across a lot of these large language model companies. So the way that products get into the hands of their end users is fundamentally changing. He talks about how Rogo is doing it. So if you're a go-to-market practitioner, this will also be very interesting for you. Let's get into it.
Rahul, thank you so much for joining the show.

**Rahul Rekhi** (2:46)
Thanks for having me, CJ. Excited to be here.

**CJ Gustafson** (2:48)
This isn't as great of an environment as the roof deck we met on when we did that panel for Tabs. I hope my virtual studio is accommodating.

**Rahul Rekhi** (2:56)
That was a particularly special one, but this is special in its own way.

**CJ Gustafson** (3:00)
You're a renaissance man in many ways because you've been in banking, you've been in policy, you've been in investing, and now you're at the forefront of AI. I'm curious what pattern you saw that made you think that finance was going to be one of the first truly industries to verticalize AI, because I think everyone saw customer support, but AI, I don't think that was at the forefront for a lot of people.

**Rahul Rekhi** (3:22)
The first thing I'll say is I've lived it, and I was, as you said, an investment banker for many years. I saw firsthand, initially, as a junior investment banker, and then as someone with more seniority, the myriad frictions and inefficiencies that existed up and down the value chain in financial services. You know, on the low end, you hear about all these stories about investment banking analysts and associates who are spending all nighters, producing PowerPoint presentations and Excel models, et cetera. And I, you know, live that. But it's also true on the other end. If you look at, for example, all the corporate finance research on merchant acquisitions, the consistent finding is that on average, an M&A deal destroys value for the acquirer. And so whether it's stock partnership or just basic deliverable creation, I've always seen finance as an area where there's so much room to improve the work product and how it's delivered. What's also true is finance is a sort of goldilocks zone for applied AI. It's an industry where the marginal willingness to pay for intelligence, for inference is high, but it's also deeply regulated and enmeshed with domain-specific technology from often decades prior business practices, habits, culture that requires wholesale transformation. Therefore, it's a great sandbox to bring AI into workforce evolution and reinvention. So both of those forces, I think, got me really excited about bringing AI to finance.