4.1. Product-market fit
Broadly speaking, latest surge of AI-driven products can be grouped into two categories.
The first includes AI features integrated into a broader service, supplementing its existing value. For instance, consider Box enhancing its platform with natural language search, Zoom introducing transcription services, or Notion integrating an AI assistant to facilitate content creation. Here, even without the AI element, these products would still function.
The second category represents entirely new products, with AI serving as the cornerstone. Without it, these products cease to exist. ChatGPT and Playground, an online AI image creator we’ve already mentioned, are examples.
This stands in contrast to the 2015’s influx of natural language processing related products, which largely remained at the tech demo stage. But I’ve noticed a tendency to overstate the product-market fit of Generative AI because of the first category of products. Many argue that AI’s product-market fit is clearer than, say, that of cryptocurrency, given the surge in companies adopting LLMs or Stable Diffusion. I find this argument superficial. While it’s true that AI is increasingly incorporated into every service, often even when it’s not necessarily that beneficial, it’s rarely the fundamental component.
In my opinion, we’re nowhere near a consensus on product-market fit of AI products creating novel value propositions or business models. They’re in the nascent stages, and it’s unclear whether their current business models can survive. I anticipate seeing as many rise and fall in the fully-AI companies as we’ve observed in the cryptocurrency realm. (And much like cryptocurrency, many of the current winners appear to be infrastructure companies.)
The market seems to confirm that. Recently, Sequoia has followed up on its one-year-old hypothesis regarding the game-changing potential of generative AI. The firm’s primary insight? While generative AI has no shortage of use cases or customer interest, it’s struggling to maintain user retention and daily engagement.
In terms of one-month mobile app retention, AI-centric apps lag behind established companies. Even when it comes to daily active users as a percentage of monthly active users, generative AI apps have a median ratio of just 14%, well below the 60-65% seen in top consumer companies and WhatsApp’s 85%. (The exception lies in the “AI Companionship” category, represented by apps like Character.) In essence, the real challenge for generative AI isn’t creating demand; it’s in proving sustained value to convert users into daily members.
There’s no doubt about it—this is still the wild west.
That’s why experiments like Intercom’s Fin are particularly intriguing. Fin is an AI-powered customer service bot. At first glance, it seems to complement Intercom’s traditional value proposition but it proposes an entirely new business model. While Intercom operates on a per-seat SaaS model, Fin’s pricing is based on usage: customers pay 99 cents per resolved conversation. This suggests that, should Fin prove successful, Intercom is prepared to cannibalize its non-AI SaaS operations believing that the new model will become a better business.
It’s a riskier venture than adding another text summarization feature to an existing app.