The term "AI" is a moving target. Technologies like databases or even machine learning were once considered AI but are now just "software." In common usage, AI simply refers to the newest, most novel computational capabilities, and the label will fade as they become commonplace.
Despite hundreds of millions of weekly active users, a huge multiple of that number have tried ChatGPT but can't find a reason to use it regularly. This signals a major gap between initial curiosity and sustained product-market fit for the general population.
Traditional software GUIs are valuable because they embed expert knowledge into a structured workflow, limiting user choices to what's relevant. A blank chatbot prompt forces the user to design the entire process from first principles, a significant and often overlooked barrier to adoption.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
A new technology's adoption depends on its fit with a profession's core tasks. Spreadsheets were an immediate revolution for accountants but a minor tool for lawyers. Similarly, generative AI is transformative for coders and marketers but struggles to find a daily use case in many other professions.
The discourse around AGI is caught in a paradox. Either it is already emerging, in which case it's less a cataclysmic event and more an incremental software improvement, or it remains a perpetually receding future goal. This captures the tension between the hype of superhuman intelligence and the reality of software development.
Despite its massive user base, OpenAI's position is precarious. It lacks true network effects, strong feature lock-in, and control over its cost base since it relies on Microsoft's infrastructure. Its long-term defensibility depends on rapidly building product ecosystems and its own infrastructure advantages.
With past shifts like the internet or mobile, we understood the physical constraints (e.g., modem speeds, battery life). With generative AI, we lack a theoretical understanding of its scaling potential, making it impossible to forecast its ultimate capabilities beyond "vibes-based" guesses from experts.