Future of Software: Building on Human-Generated Data

"With LLM-originated tooling, writing complex software has become significantly easier. It hasn't become completely solved. It's still a process that requires an orchestrator - somebody who knows what the thing is going to be. And at this point, that isn't just a technical capacity. It's product management and customer development intersecting with engineering."

"If it becomes significantly easier to build products, and the act of building a software business is not as expensive anymore because it's faster and requires fewer resources - then what will be the moats of the future? Of the immediate future, where this change is already happening, and in five or ten years, when AI-generated software products are commonplace and easily built, deployed, and maintained?"

"There used to be a lot of things we could point to as moats. How hard it is to build a software product reliably and consumably and maintainably. How hard it is to translate your knowledge from an industry into a product that serves that industry. But AI systems are taking over a lot of that now. So what is left over?"

Software development is becoming significantly easier through AI and LLM tools, reducing the resources needed to build products from ten people to potentially one or two. This democratization of software creation eliminates traditional competitive moats based on technical difficulty, reliability, and industry knowledge translation. As AI-generated software becomes commonplace and easily deployable, businesses must identify new sources of differentiation. Traditional advantages like complex engineering, maintainability, and domain expertise translation are diminishing. The emerging competitive advantage lies in real-world data—proprietary information that AI systems cannot easily replicate or access, representing the remaining defensible moat in an increasingly accessible software landscape.

Read at The Bootstrapped Founder