# Teddy Wright — My Research Generated 2026-06-11 from theodorewright.dev. Every research entry on the site as one markdown file. **About:** I have a problem where I think about way too many things at once and can't quite pin one down. So writings here span evolutionary biology, game theory, philosophy, the extremity to which modern society is different than what we evolved in, and whatever else I feel connects to the strange place the earth is and how we all appeared with consciousness here and have to deal with it. **Site status (as of 2026-04-29):** Trimming the front pages and tightening the model and dashboard rosters. **Contact:** theodorewrightwork@gmail.com · https://substack.com/@theodorealan · https://github.com/theodorewright11?tab=repositories # My Research ## Can we get otherwise unattainable insights from human text and comments using AI? *2026-06-01 · upcoming · University of Utah · SPUR Summer 2026* Authors: Teddy Wright (University of Utah), Vineet Pandey (Professor, School of Computing, University of Utah), Sara Yeo (Professor, Communication, University of Utah) Exploring how AI can analyze human text and comments in research contexts. ### Abstract Abstract coming soon. --- ## How is AI permeating into the workforce and what is there to do about it? *2026-04-15 · in-progress · working paper · in progress · Utah Office of AI Policy* Authors: Teddy Wright (Utah Office of AI Policy), Alice Schwarze (Head of Research, Utah Office of AI Policy), Zach Boyd (Director, Utah Office of AI Policy · Professor, BYU Mathematics) Working paper measuring AI automation exposure across the U.S. workforce — which occupational tasks are technically substitutable, where exposure concentrates, and how that maps to wage value at risk. ### Abstract Abstract coming soon. --- ## Companion dashboard — How is AI permeating into the workforce and what is there to do about it? *2026-04-01 · in-progress · live dashboard · in development · Utah Office of AI Policy* Authors: Teddy Wright (Utah Office of AI Policy), Alice Schwarze (Head of Research, Utah Office of AI Policy), Zach Boyd (Director, Utah Office of AI Policy · Professor, BYU Mathematics) External: https://aea-dashboard-75lzrqja9-theodorewright11s-projects.vercel.app/explorer Companion dashboard for data exploration — interactive tool for digging into AI's task-level workforce exposure across occupations, work activities, and time. ### Abstract Companion dashboard to the workforce automation exposure paper. It measures how AI capabilities map onto the U.S. occupational task structure: for a given occupation, work activity, or job category, what share of the work could be affected by current AI systems — and how many workers and wage dollars does that represent. The dashboard triangulates across five independent AI scoring sources (Anthropic Economic Index conversation and API data, MCP server logs, and Microsoft Copilot exposure data) so no single methodology drives the result, and combines that with O*NET task structure and BLS employment and wage statistics. Eight pages cover occupation- and work-activity-level exploration, two-group side-by-side comparisons, time-series trends, and task-level diffs between dataset versions. Built for Utah's Office of AI Policy. Intended for policymakers, workforce analysts, and informed members of the public. The companion paper makes the substantive argument; this dashboard is the place to interrogate the underlying numbers. --- ## Why are people miscalibrated on AI? *2026-04-01 · published · University of Utah · AI + Ethics Workshop 2026 · University of Utah · AI + Ethics Workshop 2026* Authors: Teddy Wright (University of Utah), Valen Cole (University of Utah) Paper: https://theodorewright.dev/papers/AI_Miscalibration_Research.pdf A framework decomposing view formation into Optimization Target × (Information Quality × Processing Quality), with the Self-Concealing Property as key insight — identity-protective cognition is phenomenologically indistinguishable from rigorous skepticism. ### Abstract This paper develops a framework for understanding why people form miscalibrated views about artificial intelligence. We argue that miscalibration is not primarily an information deficit problem but an optimization target problem: individuals whose goal is identity preservation rather than accuracy deploy their cognitive capacity selectively, producing motivated reasoning that resists correction. We formalize this as a multiplicative model — View = Optimization Target × (Information Quality × Processing Quality) — and identify failures along each component, distinguishing between external factors that degrade information and processing independent of the individual and target-driven distortions that systematically bias both from within. We describe a critical property of identity-protective cognition, the self-concealing property, by which the bias is phenomenologically indistinguishable from the epistemic rigor it displaces, making standard interventions of "more awareness" insufficient. We further argue that miscalibration is compounded by misspecification of the object of the view itself — misattributing outcomes to AI rather than the human system, and treating "AI" as a single entity rather than a bundle of capabilities entering distinct domains. We then outline interventions organized around what organizations can change about the environment and what individuals can do given the limits of self-correction. --- ## Project Iceberg *2025-09-01 · contribution · working paper · Sep 2025 · MIT* Authors: Ayush Chopra (MIT · Project Iceberg), Santanu Bhattacharya (MIT · Project Iceberg), DeAndrea Salvador (NC General Assembly · Future Caucus), Ayan Paul (Project Iceberg), Teddy Wright (Utah Office of AI Policy), Aditi Garg (Project Iceberg), Feroz Ahmad (Project Iceberg), Alice Schwarze (Utah Office of AI Policy), Ramesh Raskar (MIT · Project Iceberg), Prasanna Balaprakash (Oak Ridge National Laboratory) External: https://iceberg.mit.edu/report.pdf Nationally recognized AI labor market simulation study, led out of MIT. Skills-centered exposure index built on a 151M-worker agent simulation. ### Abstract Artificial Intelligence is reshaping America's over $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI automates quality control in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human–AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills across 3,000 counties and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure — where AI can perform occupational tasks — not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approximately $211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approximately $1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how capabilities may spread under alternative scenarios, Project Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation. Iceberg is built with the AgentTorch framework.