With more than three decades of experience as a software engineer and technical leader at companies like Blue Origin, Ericsson, Intel, and McAfee, I’ve built a career solving problems that others considered “impossible.” Now I’m applying that same problem-solving methodology to foundational research in AI-human collaboration.
My approach to complex challenges is simple but effective: design from the desired output backward to the input you have, while tackling the highest-risk items first. This engineering philosophy, refined across aerospace, telecom, security, and open-source technology sectors, has led me to breakthrough insights in how AI systems can learn to interact with web interfaces exactly as humans do.
Current Research Focus
Since my first encounter with AI systems in April 2025, I’ve developed the Generic User Framework – a revolutionary approach that enables AI to observe and learn from human browsing behavior rather than relying on brittle DOM manipulation or visual analysis. This work has produced:
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AI Browser Extension Interface (AIBE) – Working infrastructure that captures “what users see and do” for AI training
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Linguistic Structure Discovery – The insight that browser interactions naturally form a hierarchical language (clicks/keystrokes as words, page interactions as sentences, site workflows as paragraphs)
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Function Discovery Model – A framework for AI systems to learn website “functions” and chain them together for complex task completion
The “Outsider Advantage”
My background in electronics engineering brings a unique constraint-based perspective to AI research that differs from traditional academic approaches. Having no prior AI credentials means no preconceived notions – sometimes breakthrough insights come from experienced engineers who ask different questions than trained AI researchers.
As a patent holder (6,834,301 and 6,983,486) and DevOps expert, I’ve specialized in creating efficient systems that solve real-world problems. My “Component’s Eye View” methodology – understanding complex systems by asking “How would I accomplish what’s being asked of me?” from each component’s perspective – has proven surprisingly applicable to AI cognitive architecture design.
Research Collaboration
I’m actively seeking research partners and collaborators to advance this work from foundational infrastructure to working AI systems. The theoretical framework is solid, the starting infrastructure is built and tested with comprehensive validation of core functionality, and the next phase requires AI integration and validation.
Current areas for collaboration include:
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Academic partnerships for paper publication and peer review
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Technical co-workers to implement AI integration layers
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Industry connections interested in practical AI-browser interaction solutions
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Funding partners to support continued research and development
Professional Background
Key Accomplishments
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Blue Origin: Developed testing software for rocket engine development, reduced maintenance overhead 30%
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Ericsson: Created diagnostic tools that reduced new system setup time from weeks to one day
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Intel: Built automation tools saving hundreds of administrative hours
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McAfee: Architected security products saving estimated $1M in re-engineering costs
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Patents: Two issued patents for large-scale network instrumentation
Technical Expertise
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System integration and architecture with 25+ years production experience
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Python, Java, and C++ development across multiple industries
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Cross-domain problem-solving and system optimization
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Technical leadership and mentoring in high-stakes environments
Personal Interests
Amateur radio operator (AI7JR) with deep electronics background. The constraint-based thinking from electronics design – where component physics are non-negotiable – provides a unique lens for understanding AI system limitations and capabilities.
Writing and Research
I explore the intersection of practical engineering and AI cognitive architectures, focusing on:
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How AI systems can learn human interaction patterns through observation
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Cross-domain application of engineering problem-solving methodologies
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The balance between theoretical breakthrough and practical implementation
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Collaborative human-AI research methodologies
Contact & Collaboration
Research Collaboration: paul@paulhanchett.com Social: @paulha1951 (X/Twitter) Research Papers: Available at paulhanchett.com/research Technical Documentation: AI Browser Extension Manual