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How to Fix Decades of Technical Debt

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Global Tech Debt Impedes Growth as AI, Cloud and Legacy Systems Collide

Brian Pereira (creed_digital) •
October 31, 2025    

How to Fix Decades of Technical Debt
Image: Freepik

As companies continue to rely on outdated software and legacy systems, many dating back to the 1980s, they face mounting challenges. Rising maintenance costs, increasing complexity and significant integration hurdles hold back adoption of modern, artificial intelligence-enabled applications that typically operate across hybrid, multi-cloud environments. Technical debt has evolved beyond a mere IT issue – it now poses a serious threat to business growth and competitive advantage.

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Technical debt drains companies of time, money and even customers. It arises whenever speed is prioritized over quality in software development, often driven by the pressure to accelerate time to market. In such cases, immediate delivery takes precedence, while long-term sustainability is compromised. The Twitter Fail Whale incident between 2007 and 2012 is testimony to the adage: “Haste makes waste.”

In its early years, Twitter, now X, ran on a giant Ruby on Rails monolith nicknamed Monorail. Every new feature was crammed into the same codebase. As users grew, scaling broke constantly, giving the world the infamous Fail Whale downtime screens.

This debt was the result of a speed-first culture. To correct this, X spent years migrating to a more distributed architecture, repaying debt with years of engineering investment.

The Cost Factor

Technical debt can also prove costly for organizations. In 2012, Knight Capital lost $440 million in 45 minutes due to a software deployment error. It forgot to remove old, unused zombie code; a new deployment accidentally reactivated it, sending matters spiraling out of control.

Gartner says companies that learn to manage technical debt will achieve at least 50% faster service delivery times to the business. But organizations that fail to do this properly can expect higher operating expenses, reduced performance and a longer time to market.

A prominent example of technical debt is common business-oriented language code running on mainframes to this day. For instance, the banking ecosystem in Japan clings to its COBOL-coded applications for cost and technical reasons.

At a recent industry event in Mumbai, Hans Dekkers, general manager – Asia Pacific at IBM, told Information Security Media Group that companies hold on to COBOL for technical reasons. “What is so unique about COBOL is that it talks directly to the chip, whereas modern programming languages have a lot of interfaces between them.”

The monolithic and linear way of writing code in the 1980s was followed by Agile and other more flexible and iterative project management technologies, such as DevOps and new architectures such as microservices. These allow for greater agility, flexibility, faster feedback cycles and continuous adjustments throughout the development, supporting faster delivery cycles.

Humongous Global Problem

Over the years, organizations have found ways to measure and compensate for technical debt. Yet, the problem is humongous when viewed on a global scale – an aggregated code base of billions of lines of code.

In its first-ever report titled, “The State of Global Technical Debt – 2025,” Cast analyzed over 10 billion lines of code drawn from 47,000 applications across 3,000 companies operating in 17 countries. The countries collectively represent 51% of global GDP, based on the most recent World Bank figures available as of July 2025.

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Cast research found that global tech debt levels have reached 61 billion days in repair time – that is 61 billion workdays forfeit to technical debt.

It includes software originally written and installed as early as the 1980s and still in active use, providing a unique long-term view of technical debt. It also includes languages that were popular in the 1990s and today.

In fact, the study shows that languages such as Python and JavaScript need a higher number of workdays to repair 1,000 lines of code – 4.5 days and 3.66 days. COBOL and Go require 2.05 and 2.01 days, respectively.

If the world’s nearly 25 million developers stopped what they’re doing to work solely on tech debt, they would finish sometime around the year 2034, Cast suggests.

Does innovation take a 10-year break for that? Or can automation, machine learning and AI reduce the code repair timeline?

Modern Tools

The launch of generative AI also provided some relief for developers. Thanks to their ability to regenerate code, gen AI tools also work as coding assistants. Coders can generate code using plain English prompts. The concept was termed “vibe coding.”

But modern-day AI tools take this further into the software development workflow.

For instance, IBM’s Project Bob adapts to workflows from design to deployment. According to IBM, Bob can help reduce development timelines when modernizing legacy systems or building entirely new applications.

IBM says Bob can be regarded as “your AI-first integrated development environment and pair developer: a tool that understands your intent, your codebase and your organization’s standards.”

An integrated development environment provides developers with a complete set of tools for writing, testing and debugging code in a single interface. It streamlines the software development process by combining a source code editor, build automation tools, a debugger, compiler or interpreter – all into one application. It manages access to run-time libraries that may be needed for embedding common functionality in applications.

Not Just Technical

Experts say the blame for technical debt should not be put squarely on the IT department. There are other reasons, and other forms of debt that hold back innovation.

In his blog post, Masoud Bahrami, independent software consultant and architect, prefers to use terms such as “system debt” and “business debt,” arguing that technical debt does not necessarily stem from outdated code, as many people assume.

“Calling it technical makes it sound like only developers are responsible. So calling it purely technical is misleading. Some people prefer terms like design debt, organizational debt or software obligations. Each emphasizes a different aspect, but at its core, it’s about unaddressed compromises that make future work more expensive and risky,” he said.

Referring to the Knight Capital incident, Bahrami said it was more than technical debt. “It was organizational debt: lack of cleanup, no deployment safety nets. [Eventually] the company collapsed.”

Bahrami blames technical debt on a combination of technical, organizational, cultural debts, “and even human factors that shape how fragile or resilient a system is.”

Expanding on this, he said, technical debt can be:

  • Code-level debt: Messy functions, inconsistent naming and duplicated logic;
  • Architectural debt: The modules are tightly coupled, with inadequate separation of concerns;
  • Process debt: Lack of automated testing, slow CI/CD pipelines or undocumented procedures;
  • Business debt: Unclear product requirements, scope creep or temporary feature hacks made to satisfy stakeholders.

In 2013, the launch of HealthCare.gov site failed due to heavy load. More than 50 contractors built separate modules with no alignment. The integrations were hacked together and there was no single architectural owner. This was classified as a cultural and organizational debt. The lack of unified architecture caused chaos. Stabilizing the system required massive rework.





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    References:

    "Cybersecurity Market, By Component (Hardware, Solution, Service), By Security Type, By Solution, By Application (IT & Telecom, Retail, BFSI, Healthcare, Defense & Government, Manufacturing, Energy, Others), By Region Forecast to 2032". Www.Emergenresearch.Com, 2023, https://www.emergenresearch.com/industry-report/cyber-security-market. Accessed 25 May 2023.

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    Cyber Insurance - An Investment Worth Making

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    PSL’s Easy Guide to Understanding Cybersecurity

    The world of cybersecurity can often feel like a maze of technobabble and obscure acronyms. Yet in an era where our lives increasingly depend on digital infrastructure, it's important to have a basic understanding of the terminology involved. Learning about cybersecurity can help you avoid common mistakes and limit the chance of becoming a victim of these crimes.

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    1) Cybersecurity

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    2) Malware

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    Virus

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    Worm

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    Trojan

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    Ransomware

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    Spyware

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    3) Phishing

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    4) Firewall

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    5) VPN (Virtual Private Network)

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    6) Encryption

    Encryption converts data into a code to prevent unauthorized access. When data is encrypted, it appears as gibberish unless you have the decryption key to convert it back to its original form.

    7) Two-Factor Authentication (2FA)

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    8) Darknet/Dark Web

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    9) Data Breach

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    10) Botnet

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    Closing Thoughts

    Understanding cybersecurity is not just for the tech-savvy; it's for everyone. We are all potential targets for cyber threats, and comprehension of basic cybersecurity terminology can make a significant difference in safeguarding ourselves and our digital assets.

    Employing cybersecurity measures also helps to keep your digital data safe. This might include using a VPN, enabling two-factor authentication, or simply being wary of suspicious emails to prevent phishing attacks.

    Ultimately, it's not necessary for everyone to be a cybersecurity expert, but a basic understanding of the key terms can be an empowering first step in better securing your digital life. This newfound knowledge can provide you the confidence to navigate the online world with more safety and assurance.