The Role of AI and Modern Programming Languages in Transforming Legacy Applications

Modernizing legacy applications with artificial intelligence (AI) isn't just a trend—it's becoming essential for businesses that want to stay relevant. Think of it as giving old systems a fresh lease on life.

In a Chain of Thought podcast episode, Conor Bronsdon, Head of Developer Awareness at Galileo, teamed up with Atindriyo Sanyal, CTO and Co-Founder at Galileo, and Rachelle Palmer, Director of Product Management at MongoDB. Together, they discussed how AI is changing the game for modernizing legacy applications.

Today, AI can slice through years of outdated code and technology, making operations smoother and cutting down on long-standing technical debt. Their conversation shines a light on real-world challenges of upgrading legacy systems and is a must-listen for anyone navigating AI-driven modernization.

The Implications of Legacy Applications

Legacy applications create seemingly impossible barriers for organizations in today's fast-moving tech world. As Palmer explains, “These old systems often trap companies in a tangle of outdated technologies with dwindling expertise.”

Impact on Organizational and Technical Structure

Most legacy systems run on ancient languages and frameworks, creating significant barriers to innovation. Product teams frequently struggle with systems built on decades-old technologies like outdated Java frameworks or domain-specific languages, particularly when internal knowledge has disappeared.

"You might have them using an old Java framework, and they wanna move to a newer Java framework like Quarkus," Palmer notes.

The migration path is rarely straightforward. Organizations frequently rely on external contractors because internal expertise about the legacy technology has evaporated. This not only complicates maintenance but actively inhibits innovation.

Transitioning to modern tech stacks involves more than updating languages—it requires reimagining the entire application architecture. Teams must reconfigure front-end interfaces, data layers, and middleware components, often overwhelmed by the scope of such projects.

Financial Implications of Outdated Systems

Legacy systems drain budgets. Companies burn millions just keeping these old infrastructures on life support, with Palmer describing businesses as being "captive to paying millions of dollars for the maintenance of their own applications.".

These outdated systems create technical debt that impedes progress and widens the gap between current capabilities and future goals. This stagnation limits both financial resources and scalability potential, leading to lost business opportunities as competitors race ahead with more flexible, modern technologies.

To overcome these financial burdens, organizations can focus on leveraging AI for business value, transforming their outdated systems into strategic assets. Tackling legacy applications requires a balanced approach and patience for the modernization journey.

Advantages of Modern Languages and Frameworks

When facing legacy system challenges, modern programming languages like Rust and Golang provide strategic advantages. These languages deliver superior performance and scalability that align with contemporary technological demands.

Performance Optimization and Scalability

Companies modernizing legacy applications increasingly turn to Rust and Golang for their exceptional performance characteristics. "This is an era of Rust and Golang, and for legit reasons—they run really tight on the wire. They are optimized better for CPU than erstwhile frameworks around Java," Sanyal observes.

Rust and Golang excel at delivering high performance with minimal resources—critical for applications processing massive data volumes. Rust's memory safety features prevent common bugs, creating more reliable systems.

By adopting modern languages and frameworks, organizations can better handle high-quality data for ML, ensuring their AI models are both accurate and scalable.

Alignment with Current Technological Demands

Beyond raw speed, these modern languages integrate seamlessly with contemporary development requirements. They simplify operations while preparing systems for future technological shifts. Modern frameworks make integrating advanced technologies like AI and machine learning straightforward, providing developers with a unified coding approach.

This shift aligns with the growing emphasis on data-centric machine learning, where data quality is paramount for effective AI integration. These modern tools also help developers identify and fix ML data blindspots, enhancing the effectiveness of machine learning applications.

Sanyal notes how the adoption of these technologies enables organizations to "modernize legacy applications in months...instead of five plus years," transforming what was once a multi-year proposition into an achievable near-term goal.

These languages aren't merely tools—they're innovation catalysts that enhance organizational agility and accelerate product delivery timelines.

Leveraging LLMs to Reduce Tech Debt and Manual Efforts

With modern languages in place, automation becomes key to reducing technical debt and streamlining modernization efforts. Large Language Models (LLMs) transform software development by automating routine tasks, allowing engineering teams to focus on more creative challenges.

Automating Code Documentation and Testing

Code documentation and testing—often neglected because they're tedious—are perfect candidates for LLM automation. Palmer describes the nightmare of working with undocumented legacy codebases.

She points out that when engineers face "a really old code base that is massive" without comments, onboarding new team members becomes painfully difficult. LLMs can automatically generate comments and documentation, making codebases instantly more accessible and maintainable.

Similarly, LLMs excel at generating comprehensive test suites that ensure new applications match legacy functionality exactly. This automatic test generation compresses what traditionally took months or years into minutes or hours, or days.

Additionally, testing AI agents effectively ensures that the AI tools are performing as expected and delivering accurate results.

Accelerating Modernization Processes

LLMs do more than handle documentation and testing—they supercharge the entire modernization journey. Organizations typically face mountains of technical debt when migrating from legacy technologies to modern frameworks.

Sanyal states LLMs "bridge the gap pretty well" by enabling rapid prototyping and experimentation, allowing businesses to quickly convert their logic and front-end components into optimized code. To maximize the efficiency of these AI-driven solutions, it's important to evaluate AI agents effectively.

This technology shift enables teams to focus on solving substantial business problems rather than getting bogged down in rewriting old code. The integration of LLMs into technical environments helps engineering teams slash technical debt, streamline legacy system modernization, and redirect resources toward strategically important projects.

Coinbase's Successful Transition from Legacy Tech

The why behind technology transitions matters just as much as the how. Palmer offers valuable insights into what drives these decisions. “When Coinbase moved from Ruby to Golang, they weren't simply chasing the latest trend”—they were strategically enhancing backend performance and efficiency.

Ruby served Coinbase well during their startup phase when speed-to-market was paramount. However, as their user base expanded into the millions, Ruby's performance limitations and difficulty handling concurrent processes became significant barriers.

The unpredictability of technology choices plays a crucial role in these decisions: "You choose a technology when you're first getting started. You don't know if your application is gonna have five users or five million." Palmer states. This reality underscores why technology stacks must evolve alongside the business.

Performance and Efficiency Gains

The switch to Golang delivered dramatic improvements in both performance and efficiency for Coinbase. Golang's capability to handle numerous concurrent processes without Ruby's overhead allowed the company to substantially reduce technical debt while improving system responsiveness.

This case demonstrates how aligning technical upgrades with growth strategies positions companies for sustained success. Balancing modern technology adoption with strategic planning ensures systems can support ongoing growth and innovation.

Uber's Breakthrough From Python to High-Performance Golang

Uber's migration from Python to Golang represents a masterclass in legacy infrastructure modernization. This transition wasn't merely a language switch but a fundamental shift in Uber's engineering philosophy, addressing both accumulated technical debt and the need to align with modern performance standards.

While Python excelled for early development and prototyping, as Uber scaled exponentially, it couldn't deliver the efficiency and performance required for their massive operational workloads. The company recognized "the challenge of tech debt and also keeping up with modern languages" as critical factors driving their adoption of Golang, Sanyal notes.

Performance and Efficiency Gains

The move to Golang brought Uber remarkable improvements in both performance and efficiency, critical factors for their real-time operations. Sanyal emphasizes how Golang proved "just way tighter on the wire, and it was just way faster," enabling Uber to handle substantially larger workloads with fewer computational resources.

Golang's efficient concurrency model and optimized CPU usage proved ideal for Uber's high-throughput requirements, dramatically boosting operational capacity. The transition also helped Uber overcome technical bottlenecks associated with scaling Python applications, reducing issues like tail latency that frustrate customers during peak demand periods.

Uber's journey from Python to Golang offers valuable insights for other companies:

• Adaptability is Key: Sanyal notes how “Initial technology choices may serve early needs well”, but maintaining flexibility allows organizations to pivot to better solutions as they scale.
• Prove the Value: Demonstrating clear benefits through prototypes helps convince stakeholders to support major technology shifts.
• Strategic Decisions Drive Success: Modernization isn't purely technical—it's a strategic business decision that must align with growth and scalability needs to prevent missed opportunities.

Uber's story shows that modernization requires continuous evaluation and adaptation. By embracing technologies that match their evolving needs, Uber maintained operational excellence while staying ahead in a competitive market.

The Future of AI-Powered Legacy Modernization

Tomorrow's modernization processes will further blend human expertise with AI assistance, transforming multi-year migration projects into agile transitions completed in months. This evolution promises to democratize modernization, making it accessible to organizations of all sizes rather than just enterprises with massive IT budgets.

As AI becomes more integral, AI risk management is essential to safeguard these modernization efforts. Integrating AI effectively into existing systems requires monitoring the evolving GenAI stack, and organizations can leverage platforms like Galileo to assess, monitor AI performance, and protect AI applications, effectively supporting the modernization process.

For more insights, listen to the complete Chain of Thought podcast, where Sanyal and Palmer share insider perspectives on real-world AI modernization challenges and unexpected successes. And explore other episodes of Chain of Thought for fresh insights that keep you ahead of the generative AI curve.