Before 2022, building an AI system meant building a custom tool from scratch. Every time. For every problem. A model to predict customer churn. A different one to flag fraud. Another to read contracts. Each one a separate engineering project, requiring specialized talent, months of work, and enough expected return to justify the investment. Most ideas never made it off the whiteboard.
The result was that AI delivered real value in a handful of high-stakes, high-volume industries, like banking, logistics, large-scale retail, and almost nowhere else. Not because the math didn’t work, but because the economics didn’t.
Generative Pre-trained Transformers (GPT) changed that equation. Instead of a tool built for one job, you now have a general-purpose reasoning engine that can be pointed at almost any language or data task with minimal setup. The same underlying system that drafts an email can summarize a contract, answer a policy question, or screen a resume. One investment but many applications. That’s why adoption exploded. It wasn’t a technical breakthrough so much as an economic one. The cost of entry dropped from a six-figure engineering project to a monthly subscription.
The low-hanging fruit, like writing, summarization, Q&A, review, is already commoditized. What comes next is more consequential: organizations that learn to deploy this general-purpose capability against their own proprietary data and domain expertise will build durable advantages that are difficult to replicate. The window for that is open now, and it won’t stay open indefinitely.