Why You Can't Vibe Code Your Data Infrastructure

AI coding assistants are incredible. You can describe what you want, and working code appears. For many tasks, this feels like magic. So it's tempting to think: why hire a data engineer when I can just prompt my way to a solution?

Here's the uncomfortable truth: AI can help you build things faster, but it can't replace the judgment that comes from years of watching data systems fail. And data systems fail in ways that are expensive, subtle, and often invisible until it's too late.

The "It Works" Trap

You ask an AI to write a script that pulls data from your CRM and loads it into a spreadsheet. It works perfectly. You run it a few more times. Still works. You think: I just saved $15,000 in consulting fees.

Six months later:

• The CRM API changed and your script silently started returning incomplete data
• You've been making business decisions based on numbers that were wrong for three months
• Your "customer list" has 40% duplicates because the script doesn't handle edge cases
• The spreadsheet is now 2GB and takes 10 minutes to open
• Nobody remembers how the script works or where it runs

This isn't hypothetical. This is what I clean up regularly.

What AI Doesn't Know

AI can write syntactically correct code. It can even write code that runs successfully. What it can't do is:

Anticipate failure modes. What happens when the API times out? When the data format changes? When someone runs the script twice by accident? When the source system goes down for maintenance? Production systems need graceful degradation, retry logic, and alerting. AI gives you the happy path.

Understand your business context. AI doesn't know that your finance team runs reports on the 3rd of every month, so that's the worst possible day for a pipeline to fail. It doesn't know that "customer" means something different to sales versus support. It doesn't know your CEO will lose trust in all data if one dashboard is wrong once.

Design for scale. The script that works for 1,000 records often breaks at 100,000. The approach that's fine for daily runs falls apart when you need hourly. AI optimizes for "does it work right now" not "will it work in six months when your data volume has tripled."

Handle the ugly realities. Real-world data is messy. Phone numbers in seventeen different formats. Dates that are sometimes text. Null values where you didn't expect them. Unicode characters that break everything. Duplicate records with slightly different spellings. AI-generated code typically assumes clean inputs.

Where AI Actually Helps

None of this means AI is useless for data work. It's genuinely transformative — in the right context:

Exploration and prototyping. Need to quickly test if an approach will work? AI can get you there in minutes instead of hours. This is valuable. Just don't ship the prototype.

Boilerplate and syntax. Writing SQL queries, basic transformations, configuration files — AI accelerates the mechanical parts of coding significantly.

Learning and understanding. AI is excellent at explaining concepts, suggesting approaches, and helping non-engineers understand what's possible.

Augmenting experienced engineers. Someone who knows what good looks like can use AI to produce it faster. The judgment stays human; the typing gets automated.

The pattern: AI amplifies existing expertise. It doesn't replace it.

The Real Cost of Getting It Wrong

When a marketing website has a bug, you get a broken page. When a data pipeline has a bug, you get:

• Wrong business decisions made with confidence because the numbers looked right
• Compliance violations from mishandled personal data you didn't realize was being processed incorrectly
• Lost revenue from campaigns optimized on faulty metrics
• Eroded trust when stakeholders discover they can't rely on the data
• Compounding errors as downstream systems inherit bad data

I've seen companies make six-figure decisions based on dashboards that were wrong in ways nobody noticed for months. The code "worked." It just didn't work correctly.

The Collaboration Model

The smart approach isn't AI versus professional help. It's AI with professional guidance:

Use AI to shape your thinking. Explore what's possible. Generate options. Understand trade-offs. This is where AI shines and it's essentially free.

Bring in expertise for mission-critical systems. Anything that touches financial reporting, customer data, or business-critical decisions deserves professional implementation. The cost of getting it wrong far exceeds the cost of getting it right.

Let professionals review AI-generated code. Even if you use AI heavily, having an experienced engineer review the approach catches the failure modes that AI misses. This is often cheaper than full implementation and dramatically reduces risk.

Build with handoff in mind. Professional implementations include documentation, monitoring, alerting, and error handling. When something breaks at 2 AM (and it will), you need more than code that ran successfully once.

Questions to Ask Yourself

Before deciding to AI your way through a data project:

• What happens if this breaks and we don't notice?
• Who will fix it when it stops working?
• What's the cost of bad data versus the cost of professional help?
• Do we have someone who can evaluate whether the AI output is actually correct?
• Is this a prototype or a production system?

The Bottom Line

AI tools are the most significant productivity enhancement for technical work in decades. Use them. But use them as tools, not as a replacement for judgment.

The person who prompted the code isn't accountable when it fails at scale. The person who built your data system properly thought about the failures before they happened.

For experiments, prototypes, and exploration: prompt away. For systems your business depends on: invest in expertise. The cost of professional help is visible and predictable. The cost of production failures is neither.

Want to explore how AI can augment (not replace) professional data work? Let's talk.