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Shrijith Venkatramana
Shrijith Venkatramana

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The AI Code Blindspot Problem: Why Your Tech Team is Losing Their Grip on Your Codebase

AI writes 20% of your code today. Soon, it’ll be 50%, then 75%—and perhaps even more.

As AI-generated code proliferates, your engineers and CTO risk losing intellectual control of the codebase.

Everything appears seamless during development: mountains of code are written with AI, shiny demos are built in days instead of months, and teams marvel at AI’s capabilities.

But what happens when this code reaches production? Imagine a 2 AM outage. How will engineers—tasked with keeping applications online—diagnose issues in AI-generated code they barely understand?

The result? Extended downtimes, customer churn, and tangible business harm.

How can forward-looking tech teams counter this *"Code Blindspot Problem"? *


How Black-Box Systems Harm Organizations

  1. Onboarding: How do engineers quickly ramp up on systems dominated by AI-generated code?
  2. Institutional Knowledge: How do teams maintain context amid AI’s code volume and velocity?
  3. Technical Debt: How do organizations address hidden technical debt in AI-generated code?
  4. Trust: How do companies explain AI-driven systems to customers—or regulators?
  5. Retention: How do you hold engineers accountable for systems they neither built nor fully comprehend?

When "Control" Becomes Impossible, Build Tools to "Manage"

Traditional programming thrived in a deterministic world—total control, predictable outcomes. AI, however, operates in a non-deterministic realm: the same input can yield different outputs. Sometimes it mimics creativity; other times, it surprises.

Managing AI is less like programming and more like human management. We can infer behavioral patterns but never guarantee exact outcomes. Success lies in leveraging AI’s upside while mitigating risks.


Managing AI Systems Effectively

While AI internals remain opaque, its artifacts (code, logs, inputs) are inspectable. The solution? Better tooling to:

  • Slice and dice AI outputs for insights
  • Map system behavior at scale
  • Demystify production issues

The "set-and-forget" dream is unrealistic. Instead, deploy AI tools to understand AI code:

  • AI-assisted code reviews
  • AI-driven bug detection/fixes
  • AI-powered API/UI analysis

Every development tool—from databases to monitoring—must evolve to operate at higher abstractions.


Hexmos’ Vision: Bridging the Understanding Gap

At Hexmos, we’re tackling this challenge head-on. Our first product, LiveAPI, addresses a critical pain point: as AI-generated and legacy codebases grow, teams lose visibility into their APIs.

How LiveAPI Works:

  • Autonomously tracks all code (human/AI-generated)
  • Maps every API endpoint, parameter, and dependency
  • Provides engineers with discoverability and managers with system-wide visibility

No more Swagger limitations. LiveAPI scales with your codebase, offering real-time insights without manual intervention.


The Future of AI-Driven Development

The acceleration is inevitable. To ship faster, personalize deeply, and retain control, teams need tools that:

  • Abstract complexity without sacrificing oversight
  • Explain AI artifacts at scale
  • Evolve alongside AI’s capabilities

Hexmos is committed to this future—where "technical management" replaces "technical control," empowering teams to thrive in an AI-abundant world.

Our metric for success? Helping you ship more, ship faster, and deliver software tailored to every individual—whether your engineers or your customers.

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