In Formula 1, speed is measured not just in lap times but in how quickly teams, engineers, and fans can interpret and act on data. Every millisecond counts, making rapid decision-making essential. At the recent Singapore Grand Prix, we observed AWS’s FastTrack program in action, highlighting how AWS’s AI-native cloud is helping F1 improve both racing and fan experiences. By providing real-time race analytics and supporting advanced aerodynamic simulations for car design, AWS demonstrates the importance of fast, reliable data processing and a culture of continuous innovation in keeping F1 at the forefront of technology.

The Data Challenge Behind F1’s Speed Advantage

F1’s competitive edge now hinges on data as much as driver skill. Each car carries over 300 sensors, generating 1.1 million telemetry data points per second during a race, and teams must interpret in milliseconds while broadcasters translate complexity for fans. Cloud‑native analytics, AI, and HPC have become essential; within that stack, AWS tools play targeted roles:

  • Real‑time race intelligence depends on low‑latency ML and streams. Teams need rapid inference on live telemetry to inform pit windows, tire strategies, and overtaking probability. Amazon SageMaker supports model development and deployment for these predictions, while its Track Pulse, as a serverless data pipeline consolidates multi‑source feeds to surface signals for teams and produce broadcast overlays that explain race dynamics to viewers.
  • Aerodynamic iteration scales with elastic HPC for Computational Fluid Dynamics. Car design improvements come from running thousands of Computational Fluid Dynamics (CFD) simulations to explore trade‑offs quickly like aerodynamics improvements and drag reduction. AWS’s HPC capabilities enable engineers to execute thousands of CFD workloads in the cloud, accelerating iteration cycles relative to physical wind‑tunnel time and contributing to regulations and designs that have delivered closer racing and 30% more on‑track overtakes.
  • Fan engagement is moving toward interactive, generative experiences. Beyond live stats, audiences expect to explore “what‑ifs.” AWS showcased “Real‑Time Race Track,” letting fans create custom circuits and test strategies, while cloud streaming underpins F1TV’s high‑speed delivery with peak speeds at 6 Tbps. Together, these capabilities turn complex race data into accessible, personalized experiences without interrupting live coverage.

Methods and Culture Are Key To Innovation

While visiting the newly launched AWS Innovation Hub, we reflected on these simple truths:

  • Method and culture accelerate the journey from idea to impact. The AWS Innovation Hub in Singapore is structured along the anatomy of a digital transformation journey from concept to scale, including process, culture and mindset shift. Real demonstrations like anti-fraud detection, agentic loan underwriting, virtual try-on, self-service retail, and digital twins with partner collaboration prove experimentation translates into operational value when paired with disciplined process.
  • AWS’s “6C” framework delivers only when tied to business outcomes. Customer obsession, culture, capability, collaboration, curiosity, and courage mean little without clear metrics. Effective teams work backwards from real customer problems: define success early, test assumptions fast, and scale what works. The goal isn’t just endless experimentation. It is about embedding a repeatable cycle of ideation, validation, and deployment that compresses time to value.

What AWS Should Do Next To Further Speed Up

While AWS has made strong progress in APAC, three priorities stand out for sustaining momentum in a region where regulatory complexity and AI adoption are accelerating:

  • Augment sovereign cloud offerings. Competitors such as Alibaba Cloud, Tencent Cloud, and Huawei Cloud provide integrated stacks across infrastructure and various development domains like data and AI for on-prem/private cloud deployment for regulated sectors. AWS must accelerate hybrid and sovereign-ready solutions to meet compliance requirements without slowing innovation.
  • Provide clear roadmaps for AI models. APAC customers need visibility into model availability, localization plans, and compliance features. More clarity on the positioning and roadmap of its Nova models and others will help enterprises plan long-term investments and reduce uncertainty.
  • Improve the unified context layer for agents. As multi-agent systems gain traction, fragmented context management creates inefficiencies. Amazon Bedrock AgentCore aims to deliver a standardized layer for sharing state, memory, and reasoning securely across applications, simplifying orchestration and improving personalization. Its GA version was just released, and we are expecting more enterprise readiness in a range of areas such as multiagent context sharing, context federation across tools, and VPC integration for runtime environments.

 

If you’d like to dive deeper set up an inquiry or guidance session with Charlie Dai (AI-native cloud, AI platforms, agentic AI architecture), and Leslie Joseph (Agentic AI, intelligent automation) for a conversation.