The SUO Codex:Search User Optimization, from Algorithm Obedience to Human Resonance
Description
The SUO Codex is the canonical definition of Search User Optimization (SUO), a discipline that relocates where optimization begins. Search engine optimization begins with the query. Search User Optimization begins with the mind that produced it.
SUO is not Search Experience Optimization (SXO), which improves the encounter with a page after arrival, and it is not user-first optimization, a set of practices for serving a visitor once they are on site. Both begin after the person shows up. SUO models the person before the query is typed.
The Codex sets out the SUO Stack, the Five Layers of Resolution, and the I.N.S.I.G.H.T. checklist, grounding each in established research on information seeking and cognition: Robert Taylor's four levels of information need, Nicholas Belkin's anomalous state of knowledge, Carol Kuhlthau's information search process, Pirolli and Card's information foraging theory, Bartlett on reconstructive memory, Sweller on cognitive load. It then applies that foundation to the retrieval and selection layer now standing between a person and an answer, including parametric memory, retrieval-augmented generation, and the economics of grounding.
Files
SUO_Codex.pdf
Additional details
References
- Belkin, N.J. (1980). Anomalous States of Knowledge as a Basis for Information Retrieval. Canadian Journal of Information Science, 5.
- Kuhlthau, C.C. (2004). Seeking Meaning: A Process Approach to Library and Information Services (2nd ed.). Libraries Unlimited.
- Pirolli, P. and Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643-675.
- Simon, H.A. (1971). Designing Organizations for an Information-Rich World. In M. Greenberger (Ed.), Computers, Communications, and the Public Interest. Johns Hopkins Press.
- Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
- Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2).
- Sweller, J., van Merrienboer, J.J.G. and Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3).
- Miller, G.A. (1956). The Magical Number Seven, Plus or Minus Two. Psychological Review, 63(2), 81-97.
- Cowan, N. (2001). The Magical Number 4 in Short-Term Memory. Behavioral and Brain Sciences, 24(1).
- Bartlett, F.C. (1932). Remembering: A Study in Experimental and Social Psychology. Cambridge University Press.
- Taylor, R.S. (1968). Question-Negotiation and Information Seeking in Libraries. College and Research Libraries, 29(3), 178-194.
- Chen, T. et al. (2024). Dense X Retrieval: What Retrieval Granularity Should We Use? EMNLP 2024.
- Liu, N.F. et al. (2024). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics, 12.
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K. and Deshpande, A. (2024). GEO: Generative Engine Optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5-16.
- Zhang, P., Ye, Q., Peng, Z., Garimella, K. and Tyson, G. (2025). Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines. arXiv:2512.09483.
- Shumailov, I. et al. (2024). AI Models Collapse When Trained on Recursively Generated Data. Nature, 631.
- Sullivan, D. and Mueller, J. (2026, January). Search Off the Record [Podcast]. Google Search Central.
- Pew Research Center (2025, July). Google Users Are Less Likely to Click on Links When an AI Summary Appears in the Results.
- SparkToro and Jumpshot (2019). How Much of Google's Search Traffic Is Left for Anyone but Themselves?
- Ahrefs (2025, May). Brand Web Mentions and AI Overview Visibility: A Study of 75,000 Brands.
- Petrovic, D. (2025). Grounding Analysis Across 7,060 Queries via the Vertex Search Grounding API. DEJAN Marketing.
- United States v. Google LLC (2023, October). Trial testimony of Pandu Nayak on Navboost and Glue. United States District Court for the District of Columbia.
- Google Search API documentation leak (2024, May). Internal ranking system documentation.
- Linehan, L. and Law, R. (2026). Retrieval-Augmented Generation (RAG) Explained: How AI Decides Which Pages to Search and Cite. Ahrefs. https://ahrefs.com/blog/retrieval-augmented-generation/
- Shapow, D. Solving the Anomaly. LinkedIn. https://www.linkedin.com/pulse/solving-anomaly-dorron-shapow-djd6c
- Shapow, D. The Query Has an Answer. The Person Has a Question. LinkedIn. https://www.linkedin.com/pulse/query-has-answer-person-question-dorron-shapow-feahc
- Shapow, D. Chunking Your Content for AI? 1956 Called. LinkedIn. https://www.linkedin.com/pulse/chunking-your-content-ai-dorron-shapow-mvffc
- Marion, M. and Shapow, D. The Gospel of Brand Authority. LinkedIn. https://www.linkedin.com/pulse/gospel-brand-authority-dorron-shapow-iongc