Tech OpEd #1 — Has ChatGPT Found Product-Market Fit
What is “Product-Market Fit”?
Marc Andreessen, co-founder of venture capital firm Andreessen Horowitz, and co-founder of Netscape is often credited as developing the concept of Product-Market Fit. This concept, simply stated, is when a product becomes capable of satisfying the needs of its target market. However, successfully measuring product-market fit can be difficult. Most rely on a combination of quantitative and qualitative metrics to measure whether a product has captured its place in a market. Some quantitative metrics include churn rate, the rate at which you are losing customers, growth rate, how many new users you are gaining over time, and market share, what portion of your target market you are capturing compared to competitors in the same space. Some qualitative metrics include word of mouth, how eager your users are willing to proactively market your product for you, and coverage of your product in media and popular culture.
What is the “market” for Chat GPT?
One might argue that the direct market for Chat GPT is the Generative AI market. However, because generative AI has so many potential applications, one needs to further evaluate how Chat GPT is employing its use of generative AI. Specifically, Chat GPT in its current user-facing form is largely serving the overall “search” market. This market includes competitors like Google, that operate in the traditional search market, but also other smaller competitors that have search functionality, but do not strictly define themselves in that way. These smaller competitors would include Yelp (search for restaurants), TikTok (search from other user opinions), YouTube (video search), Wikipedia (information search), and others. Chat GPT leverages these existing products, and all others for data to condense into chat-like responses that one can then subquery to get additional information. Chat GPT’s main competitive advantage over traditional search is the ability to form these subqueries in the forms of conversations, such that users feel like they are having conversations with subject matter experts.
Chat GPT gained traction so quickly because it was one of the earliest, functional, and most observable applications of generative artificial intelligence. That is, its responses seemed like users were talking to another person. It was one of the earliest entrants of the market that showed broad applications of generative AI. Other early “chat bots” were clunky, limited in scope, and did not use the entire web as its data source. Chat GPT is functional because it is able to digest the information it scraped into logical answers; answers that others would also think of after conducting their own in-depth and time consuming research. Finally, Chat GPT’s initial impact was observable. Individuals who used Chat GPT saw it as one of many potential use cases for generative AI that was viable. People were excited by the prospect of not only Chat GPT’s potential future uses, but many other potential use cases for generative AI. Chat GPT in its current form is desirable because it can condense vast amounts of information down into digestible answers, enabling its users access to more information and subqueries that traditional search do not allow. Chat GPT is feasible nowadays largely because there is so much data on the web. It is viable because the Chat GPT model has been fine-tuned well enough to condense this information into logical responses. That is, supporting technology has evolved sufficiently to allow a large language model like Chat GPT to be functional.
Has Chat GPT found product market fit at this time?
Using Andreessen’s most literal definition of product/market fit, “Product/market fit means being in a good market with a product that can satisfy that market,” Chat GPT has found product/market fit. That is, it operates in a large and quickly growing market of generative AI. Otherwise, it also operates in a large and growing search market as users often seek alternative sources of information other than traditional search engines. Furthermore, generative AI enthusiasts and younger, more tech-savvy users are convinced that generative AI chat bots like Chat GPT are here to stay and are enthusiastic to market for Chat GPT. This evidence of enthusiasm and users marketing for the product indicates that Chat GPT has achieved product-market fit. However, declining user rates for Chat GPT imply that Chat GPT may have reached the chasm before widespread adoption. Given this stagnation and recent decline in monthly users, Chat GPT will likely not be able to cross the chasm to widespread adoption and replace traditional search.
Instead, the key to true Chat GPT realizing true product-market fit relies on Chat GPT’s successful integration with technologies that users are more comfortable and familiar with. Alternatively, the key to true product-market fit depends on Chat GPT leveraging its underlying large language model for other use cases. The technology and data that it relies on to generate its predictions is powerful. Its ability to ingest and process this quantity of information can result in massive efficiencies for various businesses that are attempting to process massive amounts of granular, individual level organized and unorganized data. Chat GPT must surpass its current form as a chat bot and move out of the search market in order to develop the generative AI market and dominate the overall information and data market.
