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EDB, a leader in Postgres data and AI, asserts that success in AI requires careful design, continuous learning, and strategic decision-making—just like an F1 team optimizing its car for every race.

In the 2024 Formula One season, 1,444 laps were completed across 24 races. Regardless of whether a Red Bull, McLaren, or Ferrari enthusiast, any avid fan would know that although the gaps between cars seem small in a single race, those differences compound over time and determine who stands on the podium. Numerous factors contribute to a driver's win, but overall, with each team operating with similar resources and developing their cars in specialized areas like the Motorsport Valley in Britain, the sport feels like a moderately level playing field.

Artificial intelligence (AI) isn't like that.

AI development isn't a capped investment category. It doesn't follow predictable rules like Formula One regulations either. "The race to dominate in this field is high-stakes and constantly changing. Three architectural design principles must be considered to achieve success with AI that is sovereign, scalable, and secure," says Kevin Dallas, CEO at EDB. Much like an F1 car's aerodynamics and powertrain, they determine long-term performance.

Design Principle One: AI Requires Data Sovereignty and Observability

Data control, availability, and security—these are the foundation one needs for AI success. Imagine an F1 car. It's built from proprietary components that are meticulously designed for maximum performance. Just like this vehicle, Dallas says that AI must be fueled by proprietary data that is protected, accessible, and compliant with regional and industry regulations.

"It's your data, it's your AI. You should have full control and access where, when, and how you need it," says Dallas. "Many assume that the idea of data sovereignty is simply about geography — 'Where is my data stored?' But that's just one part of the engine. Real sovereignty is about how data flows, evolves, and ultimately, who is at the wheel of control."

Like an F1 car that must perform in the sweltering heat of Dubai or rain-soaked track in Suzuka, AI models must operate across diverse environments with this level of control and performance.

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Observability and governance are also essential. Organizations can't optimize their AI systems effectively without AI real-time insights into data movement and usage. AI teams must have complete visibility into their data estate just as an F1 team monitors every aspect of their car's performance, may it be fuel efficiency, tire degradation, or aerodynamics. It's a secure, hybrid, and sovereign data strategy that enables AI models to operate efficiently without being locked into siloed cloud environments.

"Data is the lifeblood of high-performance racing, just as it is for AI—especially generative AI (GenAI). Organizations need observability across their entire data ecosystem to leverage its full power. A competitive edge comes from seeing data in action, in real-time," says Dallas.

Design Principle Two: Continuous Learning is the Key to AI Evolution

AI's trajectory (especially that of GenAI) is unpredictable. Technology is advancing at a pace so rapid that it's projected to contribute 15% to the economy of the United States within the next 1,000 days. Government initiatives like Stargate and the industry shift toward AI-driven agent interactions further illustrate the unprecedented pace of transformation.

Given this context, it's only right to shed light on the lack of capacity of traditional data infrastructures to support AI's demand for large-scale learning. Modern AI environments need immense computational power, multi-source data integration, and adaptive learning mechanisms, unlike legacy systems. After all, AI models consume data at a rate 10 times greater than traditional applications and require flexible and open learning ecosystems.

Sovereign data and AI platforms like EDB Postgres® AI at the enterprise level are proving instrumental in AI's evolution. EDB research from 2024 highlights the impact of estate observability in AI adoption:

  • Organizations with full data visibility are 27% more likely to integrate AI into existing workloads.
  • They are 15% more likely to enhance products using AI.
  • They have a 10% better understanding of AI deployment challenges.
  • They are 83% less concerned about public cloud data exposure.
  • They manage technology debt with up to 83% greater efficiency.

Essentially, observability and adaptability are critical to an AI's learning arc. If F1 teams continuously refine their cars through real-time data analysis, AI systems must also operate in environments that enable constant optimization and innovation.

Design Principle Three: The Need for Optimization

Indeed, no one can predict AI's long-term trajectory, but the immediate return on investment (ROI) for organizations in AI is already evident. Industries have been reshaped by GenAI, machine learning (ML), and autonomous systems (and at a pace rarely seen before).

In Formula One, marginal design improvements create lasting performance advantages. The dominance of Red Bull and McLaren over Mercedes, for example, stems from sustained engineering optimizations rather than one-time breakthroughs. AI follows a similar pattern. It requires ongoing adjustments, real-time monitoring, and iterative improvements to stay competitive.

The Formula for Winning the AI Race

Ultimately, AI's future isn't a level playing field. Hence, organizations must take control of their data, ensure observability, and embrace continuous learning to stay ahead. AI leaders, like F1 engineers, must design architectures that optimize performance at every state. The winners will be those who treat it as a high-speed competition, where every fraction of an advantage adds to long-term success.

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