Getting Freaky in the Age of AI - Ian Duncan

Index

Pop culture has spent decades (probably actually centuries?) exploring the art of wish-making, and the modern AI has commoditized wishing machines.

At the relatively benign end of the spectrum, you have Aladdin. Aladdin gets cosmic power, but he uses the Genie strategically— to handle the mundane, to test small ideas, and to eliminate friction— while reserving his own human agency for the actual problem-solving. He gets more or less what he asks for, without malice.

In the middle, you have the classic Monkey’s Paw. You get what you asked for, but not what you intended. You wish for a million dollars, your relative dies in a horrible accident, and you get the life insurance payout. The wish is literal, but the intent is lost.

And then, at the darkest end of the spectrum, you have deals with devils, or movies like Curry Barker’s 2025 horror film Obsession. In the film, a guy buys a supernatural novelty toy and wishes that the girl he likes would love him more than anyone in the world. The toy grants the wish, but it does so in a way that feels seemingly, intentionally malicious. He gets his wish, but she becomes a psychotic, homicidal stalker who feeds him his dead cat. The wish fulfills the letter of the law in the most punishing, horrifying way possible.

Walk into any tech startup today, and you’ll see developers treating AI like the Obsession toy. They tap into unlimited cosmic power via Cursor or Copilot, prompting AI to spit out massive conglomerations of code. But without deep engineering intuition to constrain that power, they don’t become gods— they become slaves to unvalidated causality. They ask the AI for a feature, and the AI maliciously grants it. The resulting 10,000 lines of perfectly formatted, completely unmaintainable boilerplate doesn’t feel like an accident; it feels like the AI took the prompt and actively conspired to maximize their future suffering. They are trapped in a cursed situation of their own making, often not yet realizing the extent of the consequences they will soon face.

To win in the era of, you have to act like Aladdin. You have to use the AI to eliminate the mundane, reserve your brain for formulating your most sick and twisted tech opinions, and then use the AI to actualize them. You have to get weird.

The Illusion of AI Productivity

To understand why weird tech is your moat, we have to talk about what “productivity” actually means. We usually measure it by how much stuff we ship. But in software development, we aren’t shipping value when we write code— we are shipping questions to reality. And when our code eventually comes into contact with reality, we have the opportunity to learn.

The speed at which you learn is dictated not by your typing speed, but by your feedback latency. Every time you get a change in front of your users, you have the opportunity to learn something about the problem you are solving and course correct if necessary. If you are shipping huge quantities of concurrent changes, you don’t necessarily get to learn as much as you would if you were shipping fewer changes more slowly, because you can’t readily attribute beneficial outcomes to individual changes.

Herein lies the danger of the standard AI stack: LLMs allow us to generate massive batches of code instantly. You can prompt an AI to spit out 10,000 lines of standard React boilerplate in an minutes at this point. But you haven’t reduced uncertainty of the problem you are solving by 10,000x. You’ve just created a massive batch of unvalidated causality. You output a lot, but you learn nothing. That isn’t to say there’s no benefit here– you certainly can potentially land changes in front of you users faster than you could before, but you begin to verge into the realm of the Mythical Man Month.

The “Glorified Code Reviewer” Trap

If you’ve used AI to generate a massive standard-stack feature, you know exactly how this feels. You hit ‘Generate’, the screen fills with 800 lines of perfectly formatted TypeScript, and your heart sinks. You aren’t an architect anymore; you are a glorified code reviewer trapped in a Kafkaesque loop. You stare at the code, realizing you have no mental model of why it’s structured this way. You deploy it with crossed fingers and a vague sense of impending doom.

That dread? That’s the physical sensation of high feedback latency. You aren’t building software; you’re just babysitting an alien artifact that occasionally demands more RAM. To escape this trap, you have to change not just how you write code, but how you think about systems.

The Danger of Inbreeding and the Power of Cross-Pollination

If your mental diet consists entirely of Hacker News, React release notes, and Medium articles about scaling Node.js, you are engaging in intellectual inbreeding. The gene pool is shallow, and the offspring are inevitably mediocre.

If you only consume standard inputs, you will only generate standard outputs. And in the...