We found the missing AI apps! And nobody downloads them

2026-06-16 · Source: Pivot to AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

A recent NBER paper, "Writing code vs. shipping code: Productivity effects across generations of AI coding tools," claims significant AI-driven "productivity gains" in software development, primarily measured by an increase in lines of code produced. However, an analysis of the paper's findings reveals that this expansion in supply has not translated into increased app usage, downloads, or ratings across four major marketplaces. Despite a surge in new applications attributed to the "agentic-coding era," total app usage has remained flat or declined within the first three months of launch. The NBER authors suggest slow user adoption, but critics argue the data indicates a flood of unwanted, low-quality AI-generated applications, challenging the notion that increased code output equates to valuable productivity.

Key takeaway

For AI/ML product managers evaluating development tool ROI, recognize that increased AI-assisted code generation does not guarantee user adoption or market success. You should prioritize user-centric metrics like downloads and active usage over raw code output to assess true productivity. Focus your teams on delivering valuable, high-quality applications rather than merely increasing the volume of "vibe code" that users ignore.

Key insights

Increased AI-generated code output does not correlate with increased app usage or user value.

Principles

• Code volume does not equal user value.
• "Productivity" metrics must align with user outcomes.
• Marketplaces are saturated with unwanted AI-generated content.

Method

The NBER paper analyzed coding productivity by measuring lines of code and then examined app usage, downloads, and ratings across four marketplaces over three months post-launch.

In practice

• Prioritize user demand over code generation speed.
• Implement quality gates for AI-assisted development.
• Monitor app usage metrics, not just supply.

Topics

• AI Coding Productivity
• App Store Analytics
• User Adoption Metrics
• Software Development ROI
• AI-generated Applications

Best for: AI Product Manager, Product Manager, Entrepreneur, AI Scientist, Director of AI/ML, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pivot to AI.