The Proliferation Problem

I recently spent a week integrating Cloud Code into my workflow. I didn’t go in with high expectations, but I came away with a significant concern that has nothing to do with the quality of the code itself.

The tool is undeniably impressive. I’ve been building scripts to generate graph definitions for our cloud network—essentially feeding a complex data structure into an LLM so it can reason about our infrastructure. With a detailed prompt, the AI handled the boilerplate and the logic with surprising accuracy.

However, the “success” of the tool highlighted a looming industry-wide issue: Synthetic Volume.

The 15-Page README Problem

Without being prompted for depth, the AI generated a 15-page README for a relatively straightforward set of scripts. It wasn’t “bad” documentation, but it was massive.

In a world where we can generate 15 pages of documentation or 1,000 lines of code in seconds, we are hitting a point of diminishing returns. We are about to be hit by a tidal wave of generated content that many will mistake for productivity.

Why Our Current Metrics are Failing

If we continue to measure engineering success by traditional markers, we are in trouble. AI will effortlessly “break” these metrics:

  • Velocity/Flow: Will skyrocket as boilerplate is automated.
  • Commit Frequency: Will increase as agents handle incremental changes.
  • Lines of Code: Will explode as documentation and tests are auto-generated.

If a Director or Manager uses these as their primary gauge for progress, they aren’t measuring value; they are measuring computational output.

The Real Work Remains Unchanged

Volume has never been the bottleneck in high-scale cloud engineering. The real work—the hard work—remains in:

  • System Integration: How these pieces actually talk to each other.
  • Comprehensive Testing: Ensuring the “hallucinated” edge cases are accounted for.
  • Architectural Integrity: Maintaining a coherent vision across a massive footprint.

As we move toward an agentic future, our biggest challenge won’t be getting the AI to write the code—it will be managing the sheer volume of “stuff” it leaves in its wake. We need to start valuing conciseness and clarity over the raw output that these tools provide so easily.