You Already Trust Code You've Never Read
When was the last time you reviewed the assembly output of your compiler?
I'm guessing never. You write Python or TypeScript or Go, hit build, and trust that something correct comes out the other end. The compiler is a black box. You don't understand its internals. You don't need to.
As InfoWorld notes, when high-level languages first required compilers, many thought no machine could write better assembly than humans. That concern was put to rest long ago.
So why do we treat AI differently?
The Explainability Paradox
There's a growing movement demanding "explainable AI" - systems that can justify every decision they make. Gartner predicts that by 2026, 60% of large enterprises will adopt AI governance tools focused on explainability and accountability.
Fair enough for medical diagnoses or loan decisions. But for infrastructure automation?
Your React app runs on JavaScript, which runs on V8, which compiles to machine code, which runs on silicon designed by tools you've never seen. At every layer, you're trusting systems you don't fully understand. You trust them because they work. Because when they break, you can see the breakage and fix it.
Why should an AI that configures a load balancer be held to a higher standard?
From Black Box to Feedback Loop
The real question isn't "can I understand every decision?" It's "can I verify the outcome and correct mistakes?"
At NetAutomate, we've been thinking about this with NetOrca Pack. The system works in three stages:
CONFIG - The AI reads the customer's intent and generates a plan
VERIFY - It checks that plan against current state
EXECUTE - It runs the plan and records what happened
If execution fails, the error goes back into CONFIG as context. The AI generates a new plan that accounts for the failure. It tries again. This loop can run multiple times until the system reaches the desired state.
The AI might not explain why it chose a particular API call sequence. But it will tell you:
- What it planned to do
- What it actually did
- Whether the outcome matches the intent
- What went wrong if it didn't
That's not a black box. That's a feedback loop with full audit trail.
The Question We Should Be Asking
Here's what I keep coming back to:
If a system:
- Deploys what the customer asked for
- Verifies it's working correctly
- Continuously reconciles against desired state
- Self-heals when drift occurs
- Logs every action for audit
...does the engineer really need to understand how it got there?
We don't demand that engineers understand how the compiler chose its register allocation strategy. We care that the program runs correctly. We have tests. We have monitoring. We have rollback.
Why should AI-driven infrastructure be different?
Tested Results vs Explained Decisions
PwC found that enterprises with explainable AI see 20-30% faster internal adoption because employees trust the outputs. But there's another path to trust: demonstrated reliability.
Every time the system successfully deploys and the customer's intent is satisfied, trust compounds. Every self-healing recovery that works without human intervention builds confidence. Every audit log that shows exactly what happened provides accountability.
Explainability is one path to trust. Verified outcomes are another.
The compiler doesn't explain why it optimised your loop that way. It just produces code that passes your tests. That's been good enough for fifty years of software engineering.
Maybe it's good enough for AI too.