AI builds the most common version of what you ask for
Ask AI to build you a calorie tracker and it will build you a calorie tracker. Daily targets. A streak counter. A pie chart of your macros. Progress rings. Maybe a motivational nudge at 8pm if you haven’t logged dinner yet.
It’ll work. It’ll probably look decent. And it will be exactly like every other calorie app already on the App Store.
That’s not a criticism of the tools. It’s just what they do. AI is extraordinarily good at pattern-matching against everything that already exists and producing a coherent version of it. Ask a broad question, get an average of every solution out there for the same problem. Fast, and indistinguishable from everything that came before.
The problem is that most apps are already wrong about something important. They were built around an idealised user who tracks everything perfectly, never has a bad day, and responds well to guilt mechanics dressed up as features. Successful products copy each other, so when existing solutions share a flaw, AI reproduces that flaw faithfully. It learns from what already exists. It doesn’t question it.
Fuel came from a different starting point. Not “build me a calorie tracker” but a slower, more awkward question: why do people stop using calorie trackers? The answer, once you look at it honestly, isn’t laziness. It’s that daily targets turn every imperfect day into a small failure. Miss your goal on Wednesday and the app registers it. Have a big dinner on Saturday and the streak breaks. The app keeps score in a way that doesn’t match how real eating actually works.
The solution was obvious once the problem was stated correctly: weekly averages. You have a weekly budget. It doesn’t matter how you distribute it. No streaks, no daily guilt, no pretending that every day is identical.
That insight didn’t come from building. It came from thinking before building. From questioning the assumption that daily was the right unit, that streaks were a wellness feature rather than an engagement mechanic, that the user needed more accountability rather than less rigidity.
AI couldn’t have done that part. Not because it isn’t clever enough. That kind of thinking requires you to distrust the existing answers, and AI’s entire approach is to learn from them.
The skill that matters hasn’t changed. Figure out what’s actually wrong before you start trying to fix it. The tools that help you execute are better than ever, but they make the thinking more important, not less. Skip it and you’ll produce the average answer faster than you ever could before.
There are already a lot of those.