AI Magic Has a Shelf Life

 

There's a moment in every AI project that feels like pure magic. You wire up the API, send a prompt, and the AI just... does the thing.

But magic has a shelf life.

Google's research team called machine learning "the high-interest credit card of technical debt." In mature ML systems, the actual ML code is roughly 5% of the codebase — the other 95% is everything that keeps the magic alive.

**The three stages every AI project goes through:**

**Stage 1 (Week 1-4): "Holy Shit, It Works!"**

Everyone's impressed. Champions get promoted. Blog posts get written. But Gartner says 30%+ of these projects will be abandoned after proof of concept.

**Stage 2 (Month 2-6): "Wait, What Did It Do?"**

Customer complaints. Unexplainable decisions. Prompts that worked in testing fail on production data. And 91% of ML models suffer from model drift — your model isn't broken, the world changed and it didn't keep up. Just ask Zillow ($528M lesson).

**Stage 3 (Month 6+): "This Is a Nightmare."**

Spaghetti prompts. Fear of changing anything. Technical debt growing faster than you can pay it down. Stripe found developers already spend 42% of their time on tech debt — AI multiplies that.

AI introduces four types of debt traditional metrics miss: Decision Debt, Trust Debt, Drift Debt, and Compliance Debt. Governance pays them all down — from day one, not as a retrofit.

McKinsey found that two-thirds of companies remain stuck in pilot mode. The one-third that scale? They built governance from the start.

**[Read the full article with the magic decay curve and governance playbook →]( https://aictrlnet.com/blog/2026/02/ai-magic-has-a-shelf-life/ )**

**Hashtags**: #AIGovernance #TechnicalDebt #MLOps #EnterpriseAI