# Empowering Intelligence: democratizing data access and driving smarter decisions at Nasdaq

## Executive summary

NASDAQ is adapting its infrastructure and software offerings to meet the demands of high-performance compute and agentic capabilities. The presentation outlines two pillars: the physical infrastructure (from microseconds to megawatts) and the financial technology software layer. Key challenges include maintaining resilience and trust in a highly regulated, low-latency environment. The solution involves expanding data center footprints, implementing advanced cooling (liquid cooling), and building an 'intelligence layer' to provide a common, trusted interface for developing complex, agentic workflows.

## Key takeaways

- The Convergence of Compute and Agents: The industry is experiencing a convergence of high-performance compute and agentic capabilities, which is fundamentally changing trading strategies and requiring massive increases in compute power, power, and space.
- Physical Constraints and Latency: Trading is a physical, API-driven world where latency matters. The source of truth for market data can vary based on physical location, necessitating robust systems to manage multiple, potentially conflicting, data instances.
- Infrastructure Scaling for AI: To support advanced deep research and modeling (which can increase strategies from 50 to 300-500 annually), NASDAQ is expanding its data center footprint and introducing liquid cooling to handle high-density compute requirements.
- The NASDAQ Intelligence Layer: The software side is addressed by the 'NASDAQ intelligence layer,' an infrastructure and technical stack overlaying existing products (like Verafin, Axiom, and capital markets tech). This layer provides a common interface for building agentic workflows.

## Technical details

- System Resilience and Trust: In critical financial infrastructure, failure is not an option. Trust must be earned through years of production-grade success and zero downtime. This requirement dictates the need for highly robust and rigorous system design.
- Low Latency and Physical Location: The physical nature of trading means latency is critical. The source of truth for prices can be geographically dispersed, requiring systems to account for different, time-sensitive data points across multiple venues.
- Agentic Workflow Development: Developing agentic workflows requires more than just functionality; it demands a rock-solid semantic context layer and comprehensive documentation to satisfy regulatory requirements (e.g., explaining why an agent made a specific decision).
- Infrastructure Components: Key infrastructure components include expanding data center footprints, implementing liquid cooling for high-density compute, and building an overlay layer (the intelligence layer) to standardize data access and interaction.

## Practical implications

- Build-Engineers must design systems that account for extreme low-latency requirements and geographically dispersed sources of truth.
- The integration of AI/agentic capabilities requires a foundational, trusted semantic context layer and rigorous logging/documentation for regulatory compliance.
- Infrastructure planning must anticipate exponential growth in compute density, necessitating advanced cooling solutions like liquid cooling.
- System architecture must evolve from simple data processing to providing optionality and a common interface (intelligence layer) for client-built workflows.

## Topics

Financial Technology, High-Performance Computing, Agentic Workflows, Data Governance, Low Latency Systems, Data Center Infrastructure, Weights & Biases, Coreweave

Source: https://www.youtube.com/watch?v=EtsIMQrqFgE