Independent Research Lab
AI systems scale. Margins don’t.
As AI adoption grows, inference cost, model complexity, retrieval depth, and orchestration overhead quietly reshape economic viability. Most teams measure capability. Few quantify structural margin risk.
Built independently as personal research and experimentation. Developed outside professional responsibilities. No confidential or employer data is used.
Framework
A practical approach to model AI systems as economic systems — linking architecture decisions to cost, margin, and resilience under scale.
1) Architecture → Cost Map
Translate design choices into cost drivers: token budgets, retrieval depth, tool calls, caching, and orchestration.
2) Margin Resilience
Stress-test unit economics under growth and volatility: MAU growth, usage intensity, model pricing changes, and prompt drift.
3) Optimization Levers
Identify highest-leverage interventions: compression, RAG depth tuning, caching strategy, token caps, and model-tier routing.
Research tracks
Three parallel tracks that connect AI architecture, economics, and structural behavior of systems under constraint.
1) AI Systems Economics
- Cost per effective request (CPER)
- Margin resilience modeling
- Growth sensitivity analysis
- Architecture → Cost → Margin mapping
2) Economic Signal Research
- Central bank speech sentiment modeling
- Narrative regime detection
- Exploratory predictive modeling (where valid)
- Macroeconomic text analytics
3) Optimization & Decision Systems
- Multi-parameter optimization
- Constraint modeling
- Structural feedback analysis
- System resilience design
Who it’s for
This work is aimed at leaders and builders who need technical depth *and* economic clarity.
Primary audience
- CTOs deploying AI at scale
- CFOs evaluating AI ROI and structural margin risk
- Product leaders facing rising inference costs
- Investors assessing economic viability of AI-native systems
Engagement
Calm, research-first engagement — no aggressive selling language. Examples:
- Executive briefings on unit economics and risk
- Architecture reviews through an economic lens
- Research collaboration (methods, models, papers)
About
Afshar Sanam AI Lab is founded as an independent research space focused on Computational Economics, AI Systems Architecture, and Structural Modeling.
About the lab
This lab develops practical models and frameworks to evaluate economic viability under scale — linking architecture choices (token budgets, retrieval depth, caching, orchestration) to cost per effective request, break-even thresholds, and margin resilience.
About the founder
Afshar Sanam is a technologist researching the intersection of AI systems architecture and computational economics, with an emphasis on structural modeling and decision systems under constraint.
Built independently as personal research and experimentation. Developed outside professional responsibilities. No confidential or employer data is used.