The Pulse: is GitHub still best for AI-native development?

2026-04-03 · Source: The Pragmatic Engineer · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

GitHub's reliability has significantly declined to "one nine" (90% availability), experiencing issues on 3 out of 30 days or 2.5 hours of degradation daily, according to third-party status pages. This decline coincides with a massive 6x increase in infrastructure load from AI agents like Claude Code over the past three months. GitHub's CTO, Vladimir Fedorov, attributed recent outages to security policy blocks, database cluster saturation, and Redis cluster write failures, often exacerbated by failover and telemetry/configuration issues. While GitHub struggles, a startup named Pierre Computer claims its "AI-native" Code.storage platform can handle over 15,000 new repositories per minute, far exceeding GitHub's reported average of ~230 per minute. This situation suggests GitHub may have lost focus, lacks clear leadership, and is entangled in Microsoft's internal politics, prioritizing Copilot revenue over core infrastructure reliability and AI-native platform evolution.

Key takeaway

For CTOs and VP of Engineering evaluating core development infrastructure, GitHub's recent "one nine" reliability and perceived lack of AI-native vision should prompt a re-evaluation of its suitability for agentic code lifecycles. Consider exploring specialized "git for AI agents" solutions like Pierre Computer's Code.storage or preparing for potential self-hosting of Git to ensure stability and scalability for future AI-driven development workflows.

Key insights

GitHub's reliability has plummeted due to surging AI agent load and internal strategic missteps.

Principles

• Infrastructure must scale proactively for emerging loads.
• Clear leadership and focus are critical for platform evolution.

In practice

• Monitor third-party status pages for critical services.
• Evaluate AI-native Git alternatives for agentic workflows.

Topics

• GitHub Reliability
• AI Agent Load
• Infrastructure Scaling
• AI-native Development
• Pierre Computer

Best for: CTO, VP of Engineering/Data, AI Architect, Software Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Pragmatic Engineer.