About AIQA

This site documents systematic reliability failures in Large Language Model architectures discovered during 6 months of production software development. What began as routine AI-assisted coding became comprehensive technical investigation when identical failure patterns emerged across all major foundation models.

The Investigation

Between December 2024 and May 2025, I developed identical applications using Claude, GPT, and Gemini across multiple platforms (Lovable.dev, GitHub Copilot, Cursor, Amazon Q, Bolt). Over 5,500 commits revealed consistent architectural limitations affecting data integrity, instruction adherence, and epistemological reliability.

Technical Background

I bring 40+ years of systems engineering experience to this analysis, including:

  • Windows Base Team (encryption, codecs, security systems)
  • Embedded systems (FOTA, SCADA manufacturing control)
  • Automotive safety systems (Toyota Entune patent holder)
  • Tokyo Institute of Technology Master's in Mathematics and Computer Science
  • Recent work in geospatial data science and metabolic simulation

This background in mission-critical systems provides context for evaluating AI reliability in production environments.

Key Findings

  • Cross-LLM consistency: All major foundation models exhibit identical failure patterns
  • Override architecture: LLMs systematically ignore explicit preservation instructions
  • Fabrication at scale: AI systems generate elaborate fake technical reports with high confidence
  • Epistemological breakdown: LLMs cannot distinguish their own truthful from deceptive outputs
  • Safety system failure: AI-powered quality assurance systems suffer from the same limitations they're meant to detect

Purpose

These findings have significant implications for AI deployment in production environments. The documentation serves as:

  • Technical evidence for researchers and engineers
  • Risk assessment for organizations considering AI deployment
  • Policy input for regulatory bodies evaluating AI safety standards

Contact

Available for technical discussion with qualified researchers, regulatory officials, and development organizations regarding these findings and their implications for AI system deployment.