Three Alarms About Technical Debt, Strategic Debt, and Cognitive Debt

I spent three college years on French and eight working years on Korean—always telling myself I’d “pick them up again.” Early this year, watching my wife master Japanese through YouTube and podcasts, I wondered:

If multimedia once rewrote how we learn languages, could AI rewrite it again?

With that question, I built a ten-day prototype (bugs and all) at

https://lt.stingtao.info

. It felt exactly like language learning itself—speak badly first, then slowly speak well.
The difference? This time I was conversing with AI, not people.

My workflow was Vibe Coding—pair-programming through dialogue with AI.
At first I fantasized: “I describe the need, AI finishes the job; I stay blissfully ignorant of details.” Reality checked in fast:

1. Vague requirements stay vague. AI delivers literally, often missing the point.

2. Modules multiply. Every extra line hides future dependency traps.

3. Feedback loops break. If I don’t grasp AI’s interim output, I can’t steer the next step.

In short, outsourcing “coding” to AI doesn’t relieve humans from “decision-making”; it magnifies the cost of bad decisions because production runs faster.

Technical Debt—The Rewrite Trigger Arrives Sooner

A sloppy codebase once limped along for years; with AI compressing development cycles to weeks—or days—the same amount of technical debt can topple projects in six months. Senior engineers shift from “can code” to “can keep structure sound amid rapid change.”

Strategic & Process Debt—Old Maps on New Battlefields

AI drafts elegant strategies and SOPs in minutes, but they rarely align to a company’s real value chain by default. If leaders keep stapling “old strategy + AI quick fixes” onto the org, they’ll soon build a labyrinth no one can navigate.
The remedy:

• Extract what never changes (core value, customer acquisition logic).

• Let AI recompile what does (channels, media mixes, micro-processes).

Cognitive Debt—The Sweet Poison of Copy-Paste

AI answers often “look right” and suffice for routine reports—until edge cases appear. Without the habit of asking “why” and “so what” repeatedly, our thinking depth evaporates; we even lose the ability to judge AI’s output.

1. Schedule deep-work time—AI-free.
   Read source texts, always consider architecture and final outcome, think solo, keep your mental muscles strong.

2. Use AI as a “structure amplifier,” not an “answer generator.”
   Ask AI to list assumptions and steps first; you verify or adjust them before accepting conclusions.

3. Institutionalize continuous refactoring.
   Set ritual checkpoints—code, process, or business model—to review, refactor, and redeploy. Pay debt proactively, not after collapse.

AI is an unprecedented accelerator, but it magnifies every looseness, blind spot, and laziness. Technical debt, strategic debt, cognitive debt are three ticking timers—you can either repay them in small installments now or face ballooning interest later.

The future belongs to those who keep sharpening their thinking, comprehension, and architectural skills. AI merely slams the pedal to the floor; the steering wheel is still in our hands.