Brian James Curry: Enterprise AI Strategist, Marketing Economist & Applied Researcher

Bridging Data Science, Economic Theory, and Business Transformation

Brian James Curry is a Kansas City–based AI and ML applied scientist, marketing economist, and applied researcher who operates at the intersection of data science, economic modeling, and enterprise transformation. His work helps organizations move beyond surface-level analytics toward causal understanding — connecting marketing investment to business outcomes, operational decisions to economic value, and AI adoption to structural competitive advantage.

He is the founder of Vector1 Research, an independent research lab focused on marketing economics, causal systems, cognitive AI architecture, and the economic implications of intelligent systems. He also founded KC AI Lab, a data science community and incubator that grew from a small meetup into a regional platform for applied machine learning education and business application.

Brian holds a Bachelor of Science in Economics from Kansas State University, grounding his analytical work in formal economic theory and quantitative methods.

The Strategic Challenge He Solves

Most organizations are drowning in data but starving for insight. Dashboards proliferate. Reports multiply. Yet the fundamental questions remain unanswered: What actually drives our outcomes? Where should we invest next? What happens if we change course?

Brian Curry’s work addresses this gap by applying economic thinking and causal inference to enterprise analytics. Rather than simply measuring what happened, his approach models why it happened and what would happen under different strategic scenarios.

This perspective is particularly valuable for organizations facing complex marketing investment decisions, seeking to understand true ROI across channels, navigating digital transformation, or preparing for AI-driven operational change.

Professional Experience

Brian has led data science, analytics, and marketing strategy initiatives across major enterprises spanning retail, telecommunications, agriculture, hospitality, media, and consumer products.

Marketing Data Science — Tractor Supply Company

Led predictive modeling, analytics solutions, and machine learning applications for the largest rural lifestyle retailer in the U.S. (~$14B revenue, 2,000+ stores). Addressed complex operational and marketing challenges using large-scale unstructured datasets. Architected integrated dashboards connecting GA4, Tableau, Adobe, Power BI, and SEOClarity for comprehensive media performance visibility across a national retail footprint. Delivered strategic insights informing marketing investment and operational decisions.

Lead Data Scientist — Koch Ag & Energy Solutions (Koch Industries, $125B)

Directed the development of predictive models, machine learning solutions, and experimentation programs supporting commodities trading, marketing effectiveness, equipment reliability, and operational optimization. Built data-driven decision frameworks through Python-based modeling and cloud-native analytics architecture. Partnered with business leadership to translate analytical insights into strategic and operational action.

Founder & Data Scientist — KC AI Lab, LLC

Founded a data science and machine learning lab focused on community-oriented education and the application of AI/ML to societal and business problems. What began as a part-time meetup group evolved into an education platform and business incubator for data science and machine learning practitioners across the Kansas City region.

Digital Strategy & Analytics — Hallmark Cards, Inc. (~$4B)

Directed end-to-end digital strategy, analytics, and advanced modeling for hallmark.com, driving improvements in visibility, engagement, and conversion. Developed machine learning models and strategic insights using exploratory data analysis and predictive techniques. Served as Product Owner for local and site search strategy, enhancing customer discoverability and navigation across the digital ecosystem.

Search Strategy & Analytics — Garmin Ltd.

Directed search media strategy and analytics for a global technology leader in GPS and wearable devices. Built real-time performance dashboards, predictive models, and strategic analyses informing content and campaign investment across international markets.

Prior Leadership Experience

Brian’s earlier career includes strategic and leadership roles across major media, telecommunications, and technology organizations:

YP, LLC — Led digital marketing strategy and analytics for one of North America’s largest local advertising platforms, overseeing performance optimization across a national sales and media ecosystem.

AT&T, Inc. — Directed search product strategy and analytics for enterprise telecommunications, shaping product roadmap and measurement frameworks.

McClatchy, Inc. — Led marketing strategy and vertical market development for a major newspaper and media conglomerate, driving revenue growth across multiple regional markets.

Vector1 Research: Applied Research Lab

Vector1 Research is Brian’s independent research lab focused on advancing the fields of marketing economics, causal inference, cognitive AI systems, and the economics of intelligent automation. The lab produces original research, open-source tools, and strategic frameworks that bridge academic rigor with real-world business application.

Strategic Engagements

Vector1 partners with select organizations on complex analytical challenges that require deeper thinking than standard consulting engagements typically provide:

Advanced Marketing & Economic Modeling — Developing causal frameworks that answer fundamental questions about what drives business outcomes. Building simulation models that enable leadership teams to explore strategic scenarios before committing resources.

AI Strategy & Architecture — Helping organizations move beyond AI hype toward substantive understanding of where intelligent systems create value, what infrastructure is required, and how to build sustainable capability.

Complex Problem Solving — Tackling analytical challenges that don’t fit neatly into existing frameworks. Applying economic thinking, causal inference, and systems modeling to questions that require original approaches.

Research Partnerships — Collaborating with organizations on applied research initiatives that advance both business objectives and broader understanding of marketing science, AI systems, or economic dynamics.

Papilon: Open-Source Marketing Analytics Platform

Brian launched Papilon, an open-source platform for marketing analytics and simulation. Papilon provides modules for causal inference, media mix optimization, and system dynamics modeling — enabling practitioners and organizations to move beyond static reporting toward simulation-driven strategic planning.

Papilon reflects Vector1’s commitment to advancing marketing science and making sophisticated analytical methods accessible to a broader community.

Memory-Node Encapsulation (MNE): Original AI Architecture Research

Brian’s most ambitious research contribution is Memory-Node Encapsulation (MNE) — a novel data structure and cognitive architecture enabling artificial intelligence systems to develop genuine episodic memory.

Unlike traditional RAG systems that fragment memories into text chunks, or embeddings that compress meaning while discarding context, MNE captures complete experiential memories as multidimensional objects. Each memory node packages semantic content, emotional valence, graph-based relational links, and distilled learnings into a unified structure.

The research introduces the NeoCortex-M architecture, which enables:

Holistic Experience Capture — Storing complete memories with sensory data, emotional weight, temporal context, and relational links
Autobiographical Causality — Learning causal relationships through direct experience rather than statistical inference alone
Dynamic Graph Relationships — Connecting memories through typed relationships (causal, temporal, semantic, contradictory, confirmatory)
Memory Consolidation — Sleep-like processes that strengthen patterns and prune irrelevant connections
Cognitive Retrieval — Relevance-weighted memory access that mimics human recall

Experimental benchmarks demonstrate substantial improvements over traditional RAG approaches: user preference recall improved from 14% to 92%, narrative consistency from 27% to 88%, and zero-shot learning from 6% to 74%.

The MNE framework includes multiple research papers:

Memory-Node Encapsulation (MNE): A Revolutionary Data Structure for Artificial Episodic Memory
Autobiographical Causality: How Episodic Memory Enables Lived Causal Understanding
The Living Memory Graph: Multi-Dimensional Self-Reorganization

This work positions Brian at the frontier of cognitive AI architecture research, exploring how artificial systems might develop genuine understanding through accumulated experience rather than pattern matching alone.

GitHub Repository: Memory-Node Encapsulation (MNE)

The Autonomy Economy: Macroeconomic Policy Research

Brian’s research extends beyond enterprise applications into macroeconomic policy with The Autonomy Economy — a comprehensive economic framework for post-employment civilization powered by AI and automation.

Rather than treating AI-driven labor displacement as a problem to resist, the framework provides a complete technical model for ensuring human prosperity through collective ownership of automated production, intelligent distribution mechanisms, and structural economic redesign.

The model includes:

Universal Autonomy Dividend — A distribution mechanism projecting $82,000/year nominal income translating to $219,000 real purchasing power by 2040
Price Deflation Modeling — Quantifying automation’s compound prosperity effect: rising productivity combined with 58% projected price deflation across consumer goods
Merit Multiplier System — Incentive structures rewarding exceptional contributions without creating extractive wealth concentration
Government Automation Analysis — Modeling 88% federal workforce reduction through AI while improving service delivery
Full Economic Simulation — Python-based modeling tools for running transition scenarios and policy analysis

Version 2.0 revealed a critical insight: automation creates compound prosperity through simultaneous productivity gains and price deflation, projecting 5–6x prosperity improvement rather than the 2–3x typically modeled.

This work positions Brian at the intersection of AI strategy, economic policy, and societal transformation — bringing quantitative rigor to questions typically dominated by speculation.

GitHub Repository: The Autonomy Economy

Research Summary: The Autonomy Economy

Research & Thought Leadership

Through Vector1, Brian publishes research and analysis on:

Cognitive AI architecture and episodic memory systems
Marketing economics and causal measurement
AI adoption and organizational transformation
Labor economics and the future of work under automation
Agentic AI systems and autonomous decision architectures
Economic modeling and value-flow analysis

His published work appears on Medium, GitHub, and through the Vector1 platform, targeting both technical practitioners and executive decision-makers.

Core Expertise

Brian’s work concentrates on several interconnected domains:

Cognitive AI Architecture & Memory Systems — Original research into how AI systems can develop episodic memory, causal understanding, and genuine learning from experience. Creator of the Memory-Node Encapsulation (MNE) framework and NeoCortex-M architecture.

Marketing Science & Economics — Applying economic theory and causal inference to marketing measurement. Moving beyond last-click attribution toward true incrementality and ROI understanding.

Predictive Modeling & Machine Learning — Building production-grade models for forecasting, segmentation, propensity, and optimization across retail, media, and enterprise contexts.

Digital Analytics & Measurement — Deep expertise in GA4, Adobe Analytics, SEO platforms, and integrated marketing measurement architectures.

Causal Inference & Experimentation — Designing and analyzing experiments. Applying quasi-experimental methods when randomization isn’t feasible. Building measurement frameworks that isolate signal from noise.

AI Strategy & Organizational Design — Helping organizations understand where AI creates value, how to build capability, and how to structure teams for sustainable impact.

Economic Analysis & Modeling — Grounded in formal economics training, applying economic frameworks to business strategy, pricing, investment allocation, and market analysis.

Education

Bachelor of Science in Economics — Kansas State University

Brian’s formal training in economic theory provides the foundation for his approach to marketing measurement, causal inference, and business strategy. Economics is not just an academic credential — it’s the lens through which he analyzes markets, incentives, and organizational behavior.

Why Organizations Engage Brian

Economic Rigor — Formal economics training combined with applied business experience. Understands both theory and execution.

Causal Thinking — Moves beyond correlation to mechanism. Asks not just “what happened” but “what caused it” and “what would happen if.”

Enterprise Scale — Has operated in large, complex organizations. Understands the realities of cross-functional collaboration, stakeholder management, and organizational politics.

Technical Depth — Hands-on with Python, machine learning, analytics platforms, and modern data architecture. Can go deep when needed.

Strategic Translation — Bridges technical complexity and business decision-making. Communicates with executives, not just analysts.

Research Orientation — Publishes original research, builds open-source frameworks, contributes novel architectures to the field. His MNE research on cognitive AI memory systems demonstrates frontier-level thinking about how artificial intelligence might develop genuine understanding.

Futurist Perspective — Thinks beyond current AI applications to fundamental questions about machine cognition, economic transformation, and organizational evolution under automation.

Engagement Options

Executive & Leadership Opportunities

Brian is open to senior leadership roles (VP, Director, Principal, or equivalent) in marketing analytics, data science, AI strategy, or applied research where he can drive strategic impact and build something meaningful. Remote or Kansas City–based hybrid arrangements preferred.

Strategic Consulting (via Vector1 Research)

Vector1 Research partners with select organizations on complex analytical challenges, AI strategy, and applied research initiatives. Engagements focus on strategic problem-solving and capability building rather than routine implementation.

Contact

For professional inquiries, consulting discussions, or speaking requests:

Vector1 Research — brian at vector1.ai

LinkedIn — linkedin.com/in/briancurry

Medium — Published research and analysis on marketing economics, AI strategy, and organizational transformation.

Brian James Curry is a marketing economist, AI researcher, and strategist helping organizations connect data to decisions, investment to outcomes, and technology to competitive advantage.

Marketing Analytics, Marketing Science, Marketing Economist, Data Science, AI Strategy, Causal Inference, Marketing Mix Modeling, Econometrics, VP Analytics, Director Data Science, Applied Research, Digital Transformation, Machine Learning, GA4, Adobe Analytics, SEO Analytics, Media Optimization, Futurist, AI Economics, Cognitive AI, Episodic Memory, Memory-Node Encapsulation, MNE, AI Architecture, AGI Research, Kansas City, Remote, Vector1 Research, Papilon