Inside Titan ML’s Category Creation Strategy: Building the AI Infrastructure Market

Creating a new market category is harder than joining an existing one. In a recent episode of Category Visionaries, Meryem Arik revealed how Titan ML is defining the AI infrastructure space at a time when the category itself is still taking shape.

Defining an Emerging Category

The challenge starts with classification. “The category is a really interesting question. And one of the challenges of building our particular business is that the category doesn’t really exist yet,” Meryem explains. “So we struggle with how to categorize ourselves as well.”

This ambiguity creates both challenges and opportunities. “There’s a couple kind of categories that it can fit in. So one is this deployment category one is like around inference. I’ve seen in some kind of market maps an inference category, another category for serving, but none of these really seem to capture all of it because we also help with development as well.”

The Education-First Approach

In an undefined market, education becomes critical. “What we have found has worked really well with developers is less salesy and more educational piece,” Meryem shares. “We are very happy to talk to our clients about open source alternatives to what we’re doing or how they can build it themselves or the underlying technology underneath it. Because if they find that content useful, then we gain trust and we gain credibility.”

This transparency extends to their entire go-to-market strategy. Rather than trying to obscure alternatives, they embrace them as part of the educational process.

Identifying the Core Problem

The foundation of their category creation effort is a clear problem statement: “ML engineers are spending way too much of their time on building infrastructure rather than solving the problems that are really core to their business,” Meryem notes. “If we think that AI is going to be as widely adopted as we think it is, then we have a lot of work to do, and we can’t have ML engineers at every single business and every single enterprise building the same infrastructure over and over again.”

Evolution of the Market

The market’s rapid evolution has required constant adaptation. “I think what we found in the beginning half of 2023 is that businesses wanted to, quote unquote, ‘do AI,’ but hadn’t figured out how to do it,” Meryem reflects. “So there was just like a bit of a, almost a panic. And now we’re starting to see businesses have methodologies and ways that they can prove AI value.”

Building for Long-Term Position

Rather than chase short-term opportunities, Titan ML is focused on becoming fundamental infrastructure. “We would like Titan ML infrastructure and Titan ML technology to be a core and probably invisible part of almost every single LLM deployment and generative AI deployment,” Meryem explains. “We want it to be analogous to deploying a database when you don’t even really think about it, and it’s just kind of part of your stack.”

This vision shapes their entire approach to category creation. Instead of trying to be the most visible or attention-grabbing AI company, they’re working to become an essential but invisible part of the stack.

For founders working to create new categories, Titan ML’s approach offers valuable lessons. Focus on educating the market about the fundamental problem, be transparent about alternatives, and build for the long-term vision rather than short-term positioning. Sometimes the strongest category position comes not from being the most visible, but from becoming indispensable.