The long-standing trend of centralizing all data into a single warehouse is incompatible with the speed of AI. Large-scale data migrations are too slow. The future architecture will involve AI models operating closer to data sources for faster, decentralized operation.

AI agents make it dramatically easier to extract and migrate data from platforms, reducing vendor lock-in. In response, platforms like Snowflake are embracing open file formats (e.g., Iceberg), shifting the competitive basis from data gravity to superior performance, cost, and features.

For established firms like VCs, the primary challenge in adopting AI isn't change management or model selection. It's the painstaking process of migrating and cleaning decades of financial data from outdated systems to make it accessible and useful for modern AI agents.

Addressing data quality issues early in the pipeline is exponentially cheaper. Waiting until data is ready for consumption means dealing with downstream consequences like regulatory issues, poor decision-making, and customer complaints, creating a massive cost multiplier.

Top AI labs like OpenAI and Anthropic build internal data platforms with conventional tools like Fivetran and Snowflake. This indicates a modern data stack is perfectly sufficient for providing AI context, and companies don't need to build bespoke, exotic infrastructure.

Contrary to its popularity, Postgres is old technology with significant technical debt. The CEO argues that AI coding agents make it feasible to build a superior, modern operational database from scratch, breaking the industry's reliance on legacy systems.

Excel Data's CEO, Rohit Choudhary, contends that the long-held strategy of migrating all data to a central lake or warehouse is too slow for the AI era. The future is decentralized, requiring AI models to be brought to the data where it resides, rather than the other way around.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.

The traditional approach of building a central data lake fails because data is often stale by the time migration is complete. The modern solution is a 'zero copy' framework that connects to data where it lives. This eliminates data drift and provides real-time intelligence without endless, costly migrations.