Analytics can solve generative AI apps’ product problem

Large language models (LLMs) are becoming a commodity. A year after ChatGPT’s release, there’s a straightforward formula to launch an AI assistant: Stick a wrapper around GPT-4, hook it up to a vector database, and invoke some APIs based on user inputs.

But if that’s all you do, don’t be surprised when your app struggles to stand out.

Technology alone isn’t a sustainable moat for AI products, especially with the barrier to entry only continuing to go down. Everyone has access to mostly the same models, and any leaps in technical knowledge quickly get replicated by the competition.

The application layer is the true differentiator. Companies that identify and address genuine user problems are best positioned to win. The solution might look like yet another chatbot, or it might look entirely different.

Experimenting with products and design is the often neglected path to innovation.

TikTok is more than “the algorithm”

While not a generative AI application, TikTok is the perfect example of product ingenuity being the unsung hero.

It’s easy to attribute the app’s success wholly to the algorithm. But other recommendation engines are incredibly powerful, too (take it from two ex-YouTube product managers).

At their core, these systems all rely on the same principles. Suggest content similar to what you already like (content-based filtering) and recommend content that people similar to you enjoy (collaborative filtering).

TikTok wouldn’t be what it is without packaging its algorithm in a novel way: an endless stream where viewers frictionlessly vote with their swipes. With an emphasis on short-form video, this product decision amplified the rate at which TikTok could learn user preferences and feed data into its algorithm.

It wasn’t just that. TikTok also led with best-in-class creator tools. Anyone can film and edit a video directly from a smartphone; no video production experience is required.

Today, the competition for short-form videos is more about the ecosystem each app offers. Having an engaging algorithm is table stakes; you need a loyal user base, creator revenue share, content moderation, and other features to round out the platform to stand out.