The Death of the Codebase Moat

For the last twenty years, the "moat" of a tech company was its codebase. If you wrote 100,000 lines of proprietary logic to solve a problem, you had a product. Today, that moat is being drained. Large Language Models (LLMs) can now execute complex logic, write code, and summarize data in seconds via a single API call. If your software’s primary value is simply "generating an output" based on user input, you aren't a software company anymore—you are an interface for someone else's model.

The Crisis: The Shrinking Value Proposition

The "SaaS paradox" of 2026 is that as AI becomes more capable, the individual features of software become less valuable. When a user can go to a general-purpose AI and say, "Take this data and give me a marketing plan," they no longer need a dedicated "Marketing Plan Generator" app.

This is the "Value Leakage." It happens when:

●Commoditization: Your "unique" feature becomes a standard toggle in a baseline LLM.
●Interface Fatigue: Users realize they are paying for a UI that actually slows down their direct interaction with the AI.
●Low Switching Costs: If you are just a "wrapper," your customers can leave for a cheaper competitor in minutes.

The Solution: From "Wrapper" to "Integrated Intelligence"

To fix a shrinking value prop, you must stop selling the output and start selling the ecosystem. You need to transition from being a tool the user uses, to a system that works on the user's behalf.

The Three Pillars of Defensible AI

1. Proprietary Context (The "Fuel")

An LLM is a genius in a vacuum. To make it valuable, you must give it "sight." Your product should feed the prompt with data the user doesn't have—historical trends, internal company knowledge, or real-time market signals. The prompt is the product because of the data it carries.

2. Orchestrated Workflows (The "Engine")

A prompt shouldn't just result in words; it should result in actions.

Example: A legal tech AI shouldn't just "summarize a contract." It should summarize it, flag three high-risk clauses based on the firm's specific policy, and automatically draft a rebuttal email to the opposing counsel.

3. The Feedback Loop (The "Moat")

Every time a user interacts with your prompt-driven product, the system should get smarter. By capturing how users refine the AI's output, you create a "flywheel" effect. Your competitors might have the same model, but they don't have your refined logic.

My Point of View:

I believe we are witnessing a fundamental shift in the definition of "Product Management." In the past, PMs managed engineers; in the future, PMs will manage Inference. The companies that thrive won't be the ones with the flashiest AI features. They will be the ones that hide the "prompt" entirely behind a seamless, automated outcome. We must stop asking "What can AI do for our app?" and start asking "How can our app make AI do something no one else can?"