The Radical AI Founders Masterclass returned this week with a discussion between Nick Frosst, co-founder of Cohere, and George Sivulka, founder and CEO of Hebbia. Their conversation, which focused on the important considerations founders need to address when building AI products, was moderated by Salim Teja, Partner at Radical Ventures. Please note that the excerpt below has been edited for conciseness and clarity.
Salim Teja: From your experience building software products, what are the nuances that differentiate the development of AI products from non-AI products?
George Sivulka: In the past, the prevailing belief was that AI should enhance an already great product rather than be the core feature itself. However, this perspective seems to be evolving, with many products now centered solely around AI.
In our experience at Hebbia, semantic search databases are great at finding answers within data, but users often ask questions about the data itself which is much more difficult to address. Hebbia spent many cycles solving this.
To build a successful AI product, it’s essential to focus on robust task decompositions, offer transparency and insights into AI’s decision-making process, and bridge the gap between the complexity of AI and user expectations of a product.
Salim Teja: We often talk about “product-market fit” in product strategy. In your own words, how would you describe the concept and how do you think about it within your teams?
George Sivulka: I think about product-market-fit as two vectors: one being what the market thinks it wants and the other being what your product actually offers. You want those vectors to be as closely aligned as possible.
In the field of AI especially, there is a third conflating vector– “narrative” fit– where customers want to feel excited about the story of their transformation with AI. Entrepreneurs should ask themselves, “Am I building a product that is actually serving a user’s needs, or are they buying a narrative?” Both are OK, but “narrative”-market fit is very different from “product”-market fit. Right now, narrative fit is very strong for AI products. Founders should delve into their metrics and have a clear understanding of which “fit” their product is achieving.
Salim Teja: We have an audience of researchers thinking about the journey to entrepreneurship and in some instances translating their research into product innovations. What advice would you give our audience on what the experience is like and what might be helpful for them to think about as they start the journey?
Nick Frosst: I often forget, but this (Cohere) is the second company that I’ve founded. The first company was based on an algorithm I was working on for saliency predictions – taking an image and predicting where people will look in that image. In short, it didn’t really work out. We eventually sold the product for around $20,000 which, as a university student was awesome, but it wasn’t the generational company that I’m excited about building now. It also wasn’t a failure.
If you try to spin your research into a company, and it fails, you’ll probably forget about it in a few years and that’s the worst it will be. Think about why it failed, and then if you want to try something else later, you will have learned. I learned that just because an algorithm or research innovation looks cool on paper, doesn’t make it useful. It turns out that people look at billboards and where they specifically look doesn’t really matter. It wasn’t a great product, but trying to do that didn’t set me back. That’s just to say – you should try.
