So where are all the AI apps?

2026-03-12 · Source: Answer.AI - Practical AI R&D – Answer.AI · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

An analysis of PyPI data challenges the assumption that AI tools have broadly doubled software developer productivity, finding no general "AI effect" on overall package creation or update rates. While new package creation on PyPI reached over 25,000 in March 2026, nearly double March 2025, the broader trend post-ChatGPT shows no epochal revolution. Instead, the study reveals a significant and concentrated increase in update frequency, specifically for popular AI-related packages. These packages saw 21-26 median releases per year post-ChatGPT, more than double the ~10 releases for popular non-AI packages. This effect is not seen across all AI packages, nor in less popular ones. The findings suggest the primary impact of generative AI on the PyPI ecosystem is a focused burst of iteration within the AI domain itself, possibly driven by specialized skills or substantial funding and hype.

Key takeaway

For AI Engineers or product managers evaluating the impact of AI tools on development velocity, recognize that broad productivity gains are not universally observed. Your focus should shift to the concentrated activity within popular AI-specific projects, where update frequencies are significantly higher. Consider investing in tools and processes that specifically enhance iteration speed for AI-centric development, rather than expecting a general uplift across all software initiatives.

Key insights

The "AI effect" on software productivity is concentrated in popular AI-related packages, not a general industry boost.

Principles

• General developer productivity gains from AI are not broadly evident in aggregate software output.
• Increased update frequency in software can indicate active use and maintenance.
• Popularity and domain focus can significantly amplify development activity.

Method

The study analyzed PyPI data by counting new package creation and, for the 15,000 most downloaded packages, tracking median release frequency by birth-year cohorts, further splitting by AI-relevance and popularity.

In practice

• Monitor package update frequency as a proxy for active software development.
• Classify packages by domain (e.g., "AI-related") to identify concentrated development trends.
• Segment analysis by popularity to reveal nuanced productivity or investment patterns.

Topics

• PyPI Ecosystem
• Software Productivity
• AI Development
• Package Release Frequency
• Generative AI Impact
• Developer Productivity Metrics

Code references

• AnswerDotAI/pypi-analysis

Best for: AI Engineer, Software Engineer, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Answer.AI - Practical AI R&D – Answer.AI.