“An opinionated product is a product designed with a specific viewpoint or philosophy in mind, often reflecting the preferences and values of its creators.”
For decades, software evolved by adding features — small, discrete functions stitched together to serve human workflows. Each feature had a purpose, an interface, and some logic connecting it to the next one. That model worked well when products were mechanical; they did what we told them to.
But AI systems are not mechanical. They are reasoning entities — adaptive, contextual, and capable of independent judgment. They don’t just execute instructions; they interpret them. And as soon as interpretation enters the equation, logic alone stops being enough. You need opinions.
Traditional products are deterministic. Data goes in, rules run, results come out. But AI systems must decide why something matters, what to prioritise, and how to proceed when information is incomplete. That’s where opinionated design becomes essential. An opinionated product doesn’t just contain tools or rules; it carries a philosophy of problem solving. It says, “Here’s how this system believes the world should work.”
Earlier, software products grew by connecting more features. Each new piece of functionality expanded the product’s reach but also its complexity. In contrast, AI systems grow by deepening their opinions — refining the internal reasoning structure that connects tools, data, and context into coherent behaviour. Once a system holds a strong opinion, every new tool or model simply adapts to that worldview. The architecture remains focused because the philosophy remains grounded.
This shift can make opinionated systems cheaper to build and more adaptive to scale. Instead of endlessly engineering feature integrations, we define a clear worldview — a bounded way of thinking — and let the AI expand within it. In other words, we stop wiring software together and start teaching systems what we believe.
For example, an AI product might hold the opinion that every action should be context driven, that agents should collaborate like teams, and that systems should self correct when they fail. These aren’t features; they’re beliefs. And when embedded into an agentic architecture, those beliefs shape everything: how agents communicate, how failures are handled, and how creativity or reasoning unfolds.
Across much of today’s AI landscape, the gap isn’t technical — it’s philosophical. We’ve built systems that function well but often without a defined worldview to anchor them.
This moment in AI development feels like the early days of software engineering all over again — except the shift is deeper. We’re moving from “specs first” logic to “belief first” systems. The companies that recognize this will stop trying to orchestrate flows and start defining worldviews. In this new landscape, what defines a product isn’t its workflow, but the clarity of its reasoning — how clearly it understands and applies its own worldview.
It also raises a bigger question: If AI systems need opinions to function, who defines those opinions? That’s not a technical question but a personal one — about design philosophy, creative ownership, and how we encode our own values into autonomous reasoning systems.
And the strongest systems — the ones that hold the strongest opinions — will be those that know exactly what they believe, and whose beliefs are humane and shared values.
