The Changing Role of Indices
We live in a world where data has become the most valuable resource, and the emergence of Big Data, Artificial Intelligence (AI), and Machine Learning (ML) has transformed not just how we gather and store information, but how we understand and use it. In this rapidly evolving environment, one of the most foundational — yet often overlooked — components of data infrastructure is the index.
What once powered simple lookups must now support dynamic, predictive, and context-aware systems.
Traditionally, indices enabled efficient data retrieval for structured datasets and fixed query patterns. Indices in economics have their roots in the early efforts to measure changes in prices and economic activity. One of the earliest examples is the price index developed in the 18th century by William Fleetwood, who used a “basket” of goods to track inflation for Oxford students. Later, in the 19th century, economists like Joseph Lowe refined these methods into more systematic indices. By the early 20th century, national governments began creating official indices, such as the Consumer Price Index (CPI), to monitor cost-of-living changes and guide economic policy. These indices became crucial during times of economic turmoil, like the Great Depression, when understanding price trends and economic output was vital for recovery efforts.
As economies grew more complex, so did the types and uses of indices. Beyond price indices, economists developed tools like the Gross Domestic Product (GDP) deflator, producer price indices, and stock market indices (e.g., the Dow Jones Industrial Average, created in 1896). These indices help measure broader aspects of economic performance, from production and consumption to investment and market trends. I
In addition to traditional economic indicators, the United Nations introduced broader indices to capture social and developmental progress. The Human Development Index (HDI), developed in 1990, combines data on life expectancy, education, and per capita income to measure overall human well-being. Similarly, the Gender Development Index (GDI) compares the HDI for men and women to highlight gender disparities in development. These UN indices emphasize that economic performance alone does not fully reflect a country’s development, underscoring the importance of social equality, access to education, and quality of life. Together, traditional and human-centered indices offer a more complete picture of global progress.
In the modern era, indices are vital for central banks, businesses, and investors, guiding decisions from interest rate policies to inflation targeting and portfolio management. Through advancements in data collection and computing, economic indices have become more precise, timely, and central to understanding and managing modern economies.
We’re no longer asking ‘where is this data?’ — we’re asking ‘what does this data mean, and how can I use it right now?’
The Rise of Vector Thinking
One of the clearest examples of this shift is the move toward vector indexing. Modern AI models encode text, images, and other data into high-dimensional embeddings that capture semantic relationships. Searching through this kind of data requires more than a traditional database index.
Vector indices, such as those built with FAISS or HNSW, are now essential for semantic search, recommendation engines, and image recognition. Platforms like Pinecone and Weaviate have emerged to provide scalable infrastructure for building intelligent vector search into real-time applications.
Teaching Machines to Index
In 2018, Google proposed a radical idea: what if we could teach a machine to index data? Instead of relying on a fixed data structure like a B-tree, a learned index structure uses a machine learning model to predict where a piece of data will be stored.
This method not only reduces memory usage but can significantly speed up data access in read-heavy workloads. By modeling the data distribution itself, the index becomes faster and more adaptive — a prime example of how AI is reshaping even the core mechanisms of data systems.
Indexing the AI Economy: Anthropic’s Bold Experiment
Perhaps one of the most futuristic examples of rethinking indices comes from Anthropic, which developed an AI Economic Index. Unlike conventional indices that locate records, this index tracks how AI is transforming the macroeconomy, measuring productivity changes, sectoral shifts, and capability growth across models.
It’s not an index of data — it’s an index of impact. This dynamic, AI-powered system reflects how the economy is reacting to the evolution of intelligent models, offering insight that’s both strategic and forward-looking.
Features on Demand: The ML Feature Store
Another major evolution in indexing is happening in machine learning pipelines. Features — the variables used by ML models — must be stored, versioned, and accessed consistently. That’s where feature stores come in.
Airbnb and others use feature stores to index massive libraries of reusable, versioned features, enabling faster model development and deployment. In this context, indexing ensures not just speed, but consistency and trust across experiments and production workflows.
Beyond Speed: The New Indexing Priorities
In the AI era, performance isn’t just about speed — it’s about insight. Traditional metrics like query latency are now joined by concerns like model accuracy, result relevance, and real-time adaptability.
Modern indices must be context-aware, adaptable, and capable of operating across structured and unstructured data. This means indexing metadata, tracking real-time changes, and even anticipating queries based on user intent and behavioral patterns.
The Future: Indexing for Insight, Not Just Retrieval
The most exciting transformation is philosophical: Indexing is no longer a technical footnote — it’s a strategic capability. It shapes how businesses discover patterns, how scientists accelerate research, and how we as individuals interact with increasingly intelligent systems.
The future belongs to those who can build indices that think — not just point. In this new world, an index isn’t just a shortcut to data. It’s a living, learning system that helps us navigate complexity, uncover meaning, and make better decisions at scale.
Glossary of Technical Terms
Index – A data structure used to improve the speed of data retrieval operations in a database or system.
Vector Index – A structure that enables similarity search on high-dimensional data like embeddings.
Embedding – A numerical representation of data that captures relationships in a multi-dimensional space.
FAISS / HNSW / Annoy – Libraries used for fast approximate nearest neighbor (ANN) search.
Approximate Nearest Neighbor (ANN) – A technique for quickly finding close matches in large, high-dimensional datasets.
Learned Index Structure – A machine-learned model that predicts the position of a record in a sorted data structure.
Feature Store – A system used in ML workflows to store, retrieve, and manage machine learning features.
Anthropic AI Economic Index – A dynamic index developed by Anthropic to track the economic and societal impact of AI technologies.
Semantic Search – Search that understands the context and intent behind queries rather than relying on exact keyword matches.
Model Drift – The degradation of a model’s performance over time as data patterns shift.
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