Eight examples of technical debt: how software failures hurt business
In this article
Summary
Technical debt exists in every organization that relies on software, but because it’s often hidden, it frequently goes unaddressed. While technical debt takes many forms, from code-level shortcuts to skipped testing, one category dominates the damage: architectural debt. Gartner® predicts that architectural technical debt will account for 80% of all technical debt by 2027, the kind that lives in the connections between systems rather than inside any single module.
In this article, Software Improvement Group explores eight real-world examples of technical debt, six of them architectural, and shares key lessons to help you safeguard your organization from this “silent company killer.”
Introduction
It is estimated that technical debt accounts for 21% to 40% of an organization’s IT spending. For the average global enterprise, that translates to more than $370 million per year wasted on maintaining outdated, inefficient systems. In the US alone, the annual cost of poor software quality, including technical debt, operational failures, and cybersecurity incidents, has reached $2.41 trillion.
Instead of funding innovation and growth, IT budgets are quietly consumed by servicing technical debt.
The big problem with technical debt, however, goes beyond its destructive impact on IT budgets. Perhaps the most challenging aspect of technical debt management and preventing software failures is that the debt accrued is often invisible. As McKinsey puts it:
“Technical debt is like dark matter: you know it exists, you can infer its impact, but you can’t see or measure it.”
However, here’s where we disagree. In our experience, tech debt is most certainly measurable.
Tech debt harms your business in concrete ways: productivity loss, spiraling software development costs, cybersecurity breaches, and reputational damage to your brand.
As such, the best way to reduce the tech debt in your business is first learning from other businesses’ software failure examples. Doing so allows us to recognize the omnipresence of technical debt in tech companies, and, increasingly, in any company which uses software in their daily business operations.
In this article, we’ll explore common pitfalls associated with technical debt, examine eight well-known cases and their business consequences, and discuss how you can avoid making the same mistakes in the future.
What is technical debt and why does it lead to software failures?
When we talk about technical debt, we’re referring to past decisions made during software development—often involving shortcuts or compromises in code quality—to speed up time-to-market, which ultimately increases the long-term cost of maintaining the software.
To put it another way, ‘debt’ is withdrawn as short-term engineering shortcuts that help get a product to market quickly. It is repaid as ‘interest’ in the form of high maintenance costs, cybersecurity risks, productivity loss, talent loss, and more.
Beyond the financial costs of technical debt lies a more critical risk: the potential for software failure caused by engineering shortcuts. Below, we outline some common examples of technical debt and how they can lead to such failures.
- Unscalable architectures: When systems aren’t built to scale efficiently —often due to a monolithic or overly complex architecture—they can become difficult to modify or extend. Much like dealing with a tangle of cables: you’re stuck with what’s already there, making it hard to add new features or adapt to growing user demands. While the system might not crash, its rigidity can severely limit future development.
- Frequent bugs and crashes: Software systems can also crash as a result of poor quality coding practices, such as hastily copy-pasting buggy code. Bad code means a profusion of bugs, leading to defects in the software product which either crash a system or require extensive and costly downtime to fix (when they should never have been left there in the first place!). The worst part? Having customers or users inconvenienced by these.
- Slow development cycles: When an organization carries around an unmanageable amount of technical debt (which is truer for more organizations than you might think), their software development teams are prone to become bogged down with maintenance and debugging tasks. It’s like walking around with a shackle. As a result, development cycles become glacial, and team morale plummets.
Reduced productivity: As McKinsey points out, teams in companies with unsustainable tech debt levels are forced to spend significant resources on low-impact fixes. The debt here diverts an IT team’s focus away from innovation and high-priority, high-value projects—a diversion that could prove the difference between the success and failure of a software product.
- Cybersecurity vulnerabilities: Lastly, technical debt can also be found in codebases as cybersecurity vulnerabilities: open doors for cyberhackers to exploit and, with malware, data theft or any number of other malicious actions, bring your IT operation to its knees.
Of all these forms, architectural debt can be among the most damaging and the hardest to fix. A code-level bug can be patched in a sprint. An architectural problem, a monolith that can’t scale, a system with no clear owner, a patchwork of components that were never designed to work together, takes months or years to remediate. And as the examples below show, ignoring it can cost billions.
Real-world examples of software failures caused by technical debt
Too much technical debt arises from a range of suboptimal practices, including shoddy coding, messy architectural design, inadequate documentation, insufficient testing, and more. These practices often emerge under pressure to deliver quickly, and when repeated over time, they accumulate into significant organizational challenges.
Understanding this concept, let’s take a look at eight real-world examples. Six of them share the same root cause: architectural debt.
Examples of architectural debt
1. Apple’s Siri (2024-2026)
Apple is one of the world’s most valuable companies. It built Siri in 2011 as the first mainstream voice assistant, years before Amazon’s Alexa or Google Assistant existed. By 2024, Siri should have been the most advanced AI assistant on the planet. Instead, it had become one of the clearest examples of architectural debt in modern technology.
Internal reports describe Siri as a technically fragmented system built from old rule-based components stitched together with newer generative models. This patchwork architecture made updates difficult and led to frequent errors. Internal testing showed core Siri features working only 66 to 80 percent of the time.
The consequences went beyond a buggy product. Between 12 and 15 key AI researchers and executives left Apple within a single year, including the head of the AI models team, who moved to Meta. Apple previewed a “personalized Siri” in 2024 with impressive new features. It never shipped. It was pushed back repeatedly, and in March 2025 Apple publicly admitted the work was taking longer than expected. CEO Tim Cook replaced his AI chief in an attempt to reset the effort.
The price tag: in January 2026, Apple finalized a deal to pay Google approximately $1 billion per year to license a custom 1.2 trillion-parameter Gemini model and rebuild Siri’s core functionality on top of it. Reports indicate the Siri codebase was so entangled that engineers had to rebuild it from scratch on a large language model foundation. The world’s most valuable company turned to a direct competitor for help with one of its signature products, because its own accumulated architectural debt made it impossible to modernize Siri fast enough internally.
As of mid-2026, the Gemini-powered Siri is expected to debut nearly two years after Apple first promised the upgrade.
Siri was a first mover. Apple had more resources than almost any company on earth. None of that mattered. Architectural debt accumulated over 13 years of shortcuts and fragmented design, and it compounded until Apple could no longer fix the problem internally. If Apple can fall behind because of architectural debt, any organization can.
2: HealthCare.gov (2013)
When HealthCare.gov launched on October 1, 2013, it was supposed to give millions of Americans a simple way to purchase health insurance under the Affordable Care Act.
By 8:01 a.m., users were staring at blank screens and timeout errors.
The surface-level explanation was too much traffic. The real problem ran deeper. More than 50 contractors had built separate modules with no alignment and no single architectural owner. The integrations between those modules were improvised rather than designed. At the center sat a single overtaxed component responsible for coordinating identity verification and eligibility checks across several platforms, simultaneously. It was a textbook single point of failure, and it failed on day one.
The government launched an emergency “tech surge,” bringing in outside experts to deconstruct and rebuild the point of failure while keeping it running. They added the caching layers, message queues, and load balancers that should have been part of the original architecture. Retrofitting these foundational components into a failing system pushed the total cost past $2 billion, up from an initial budget of $93.7 million.
HealthCare.gov is a case study in architectural debt. The code in any individual module may have been fine. The problem was how those modules connected, who owned the overall design, and the fact that nobody tested the system as a whole under realistic conditions. HealthCare.gov showed what happens when architectural debt goes unaddressed: a project that cost more than 20 times its original budget and failed publicly on the world stage.
3. Nokia (2000s)
If you cast your mind back to the 90s, you’ll remember that Nokia was one of the world’s forerunners in mobile phone technology. So much so, that the indestructibility of the Nokia 3310 has become something of a modern-day meme, a nostalgia for a bygone age in which phones were built to last longer than it took developers to bring out a newer version.
Nokia faded into obscurity while Apple, Samsung, Oppo, and Google built the smartphones we use today. The reason was architectural debt.
With a monopoly over the mobile phone market, Nokia neglected to review its many decades of technical debt. But then along came the world’s first smartphone, the iPhone, released in 2007. Naturally, Nokia tried to adapt, but the company soon discovered that architectural debt had rendered its operating system impossible to port, scale, or modernize in line with the demands of this new smartphone hardware. The architecture that had served Nokia well in the feature phone era became a prison in the smartphone era.
Soon, Nokia was outstripped by its competitors and was forced to sell to Microsoft. But the architectural debt nightmare didn’t end there. Microsoft inherited all of the same debt that had rendered Nokia unable to compete in the first place. Despite the company’s best efforts, just two years after the Nokia acquisition, Microsoft wrote it all off to the tune of $7.6 billion, almost 8,000 layoffs, and a further $8 billion restructure.
Ignoring architectural debt costs more than fixing it, no matter whether you’re fighting to break into a market or already have a strong monopoly in your field.
4. Southwest Airlines (2022)
For Southwest Airlines, architectural debt in its crew scheduling systems was to prove the root cause of nearly $1 billion in compensation and fines, putting both a black mark on the company’s reputation and on its checkbooks.
When the holiday season rolled around in 2022, passengers of the popular US airline packed their bags and got ready to head off on holiday or return from their vacation. Little did they know that the increase in demand for Southwest Airlines came with increasing pressure on the software system used to assign crew to their flights. Built on an architecture that had not been modernized to handle peak demand, the system was unable to scale and instead crashed, ultimately stranding over 16,000 flights and all of their passengers.
Neglecting your architectural debt is like neglecting a ticking time bomb at the heart of your organization. Sooner or later, it’s going to cost you a lot of money.
5. Twitter (2007-2012)
If you used Twitter (now X) between 2007 and 2012, you probably remember the Fail Whale: a cartoon of a whale being lifted by birds, displayed whenever the service crashed. It appeared so often that it became a cultural symbol of the era. Behind the meme was a serious engineering problem.
Twitter launched in 2006 on a Ruby on Rails monolith called Monorail. It was a reasonable choice for a small startup. But by 2009, usage had grown 1,444% in a single year, and the architecture could not keep up. The Rails servers were effectively single-threaded and capable of only 200 to 300 requests per second per host. Every time a major event drove traffic spikes, from the World Cup to celebrity tweets, the system buckled.
The monolithic architecture and an overloaded MySQL database created bottleneck after bottleneck, leading to the recurring outages that defined the platform’s early reputation. Twitter’s engineering team began a massive migration in 2010, moving from the monolith to a microservices architecture built on Java and Scala. The results were dramatic: throughput jumped from 200-300 requests per second per host to 10,000-20,000, far exceeding even the team’s own expectations. By 2012, Twitter handled the US Presidential Election without a single outage. The Fail Whale was officially retired in 2015.
Twitter’s story is different from the others in this article. It is a technical debt example with a redemption arc. The company recognized the architectural debt in time and invested years of engineering effort to repay it before the platform collapsed.
The pattern is the same as every other example here: the Ruby on Rails monolith was a perfectly good choice in 2006. It became crippling debt by 2009.
Architecture decisions that make sense at one scale can become existential risks at another, and the longer you wait to address them, the more expensive the migration becomes.
6. Friendster (2000s)
You likely don’t remember Friendster, which is itself the point. Back in the early Noughties, before ‘social media’ was even common parlance, various social media startups were competing for dominance in a fast-evolving industry.
Friendster, however, was the only company with a first-mover advantage. Though rivals MySpace were hot on their heels, according to Friendster investor Reid Hoffman, “It was Friendster’s game to lose.” Yet lose they did…
Friendster’s architecture was riddled with technical debt which had gone unchecked in the glow of the start-up’s early successes. But as demand quickly grew and increasing numbers of wannabe users headed over to the site, only to find out that they couldn’t actually use it: that architectural debt emerged as criminally slow page-load speeds.
So slow, in fact, was the Friendster website that users left in their thousands, migrating instead over to the much faster pages of MySpace.
To cut a long story short, the failure of Friendster’s architecture to deliver its users an efficient and engaging experience ultimately caused the failure of the business as a whole. Even industry leaders can be brought to their knees by architectural debt; it never pays to leave it unchecked.
Examples of code-level technical debt
Architectural debt dominates the examples above, but it is not the only form of technical debt that can cause catastrophic failures. Two well-known cases illustrate how code-level shortcuts and process failures can be equally devastating in the moment, even if they are easier to remediate than deep architectural problems.
7. The Y2K crisis (1960s-2000)
All bar the very youngest members of your IT department will remember the extraordinary panic surrounding the year 2000. Developers had stored dates as two digits rather than four (e.g., ‘1970’ became ’70’) to save on storage costs. That shortcut was taken intentionally, generation after generation, from the 1960s onward.
Without the foresight to recognize the issues this might cause come the year 2000, that same code-level debt resurfaced in the late 90s, ultimately costing over $100 billion to fix, instantly erasing whatever savings had been made on memory all those years before in what has been hailed as “the most expensive peacetime catastrophe in modern history.”
Y2K was not an architectural problem. The debt was in the code itself, a single design decision repeated billions of times across millions of systems. The fix was tedious but conceptually simple: find every two-digit date and change it to four digits. The scale was what made it ruinous.
8. Knight Capital Group (2012)
After 17 years of hard work and intelligent trading, by 2012 Knight Capital Group had built itself into the largest global trader of US equities, with a market share on the US stock exchange of around 17%.
But on 1 August 2012, a software deployment went wrong. Old code was accidentally reactivated when a new trading system was installed. As soon as the New York Stock Exchange opened, the flawed software immediately began buying up stocks without authorization. The flaw managed to purchase $7 billion of stock before it could be stopped, resulting in a $440 million loss in just 45 minutes.
By summer 2013, Knight Capital was forced to sell to rival trading firm Getco LLC, marking the end of an 18-year journey in FinTech.
Knight Capital was not an architectural failure. The debt was in the deployment process: no proper version control, no automated checks to confirm deprecated code had been removed, no kill switch to halt runaway trades. Process debt is faster to fix than architectural debt, but as Knight Capital shows, it can be just as fatal when it strikes.
The AI coding experiment that built 110 person-years of debt in one week (2026)
This one is different from the examples above. No business collapsed, and no customers were stranded. But it may be the most instructive example for 2026.
In the age of AI, it feels intuitive to assume that the new agents at our fingertips can simply fix or avoid technical debt entirely. FastRender is a useful test of that assumption.
Cursor, an AI coding tool, used a swarm of AI agents to build FastRender, a browser engine with more than 3 million lines of code, in a single week. The speed was remarkable: roughly equivalent to 110 person-years of development effort compressed into seven days.
When SIG analyzed the output using Sigrid®, our software portfolio governance platform, FastRender scored 1.3 out of 5 stars for maintainability, placing it in the bottom 5% of systems in SIG’s benchmark. Its architecture quality score was 2.1 out of 5. Components were tightly coupled, modularity was low, and changes in one area could trigger failures elsewhere unpredictably.
Cursor presented FastRender as an experiment, not a production system. But it demonstrates what is now happening at scale across organizations adopting AI coding tools without quality governance in place. As Luc Brandts of SIG explained in a recent webinar:
“If you have an AI doing this, it’s like an intern, a very clever and super-fast intern, but not with a lot of experience. Without that context, you will create architectural drift.”
The FastRender experiment also puts a number on the cost of AI-generated architectural debt. Cursor’s token spend for that one experiment was estimated at €10 million to €15 million, the equivalent of 50 to 75 person-years at a fully loaded developer cost of €150,000 per year.
The output still scored in legacy-system territory for maintainability and architecture. As our State of Software 2026 report concludes: tokens do not buy architectural understanding. They produce more code that can ignore it.
AI-generated code: the next wave of technical debt
The examples above span decades, from the 1960s Y2K shortcuts to Apple’s 2024 Siri crisis. But a new category of technical debt is forming right now, and it is growing faster than any that came before it.
AI coding tools like GitHub Copilot, Cursor, and others now generate 41% of all new code written globally. Developers ship features faster, and from the outside, it looks like pure productivity gain. The reality is more complicated.
A GitClear analysis of over 100 million lines of changed code found that code churn, the percentage of lines reverted or rewritten within two weeks, increased by 39% in projects that rely heavily on AI coding tools. Pull requests per developer went up 20%, but incidents per pull request rose 23.5%. More code ships faster, but each change is slightly more likely to break something.
A peer-reviewed study analyzed hundreds of self-admitted technical debt comments left by AI coding agents and found that while AI-generated debt is slightly more detailed than human-authored debt, both types routinely describe problems without clear guidance on how to fix them. In other words, AI tools are generating debt just as readily as human developers, but at a much higher volume.
The pattern matches what we see in every historical example in this article. Speed-to-market wins in the short term, debt accumulates in the background, and the bill arrives later, with interest.
For organizations using AI coding tools (and Gartner projects 75% of enterprise software engineers will do so by 2028), AI-generated debt will build up regardless. What matters is whether you have the visibility to detect it before it compounds into a serious problem.
Conclusion
AI coding tools are accelerating this problem. SIG’s State of Software 2026 report, based on analysis of more than 30,000 systems and over 400 billion lines of code, found that AI-generated code carries more maintainability issues than human-written code, and that the gap widens as codebases grow.
A Stanford University analysis found that AI productivity gains shrink significantly as codebases grow: teams saw roughly a 60% lift in productivity at around 10,000 lines of code. By 100,000 lines, those gains had largely collapsed.
The reason is architectural context and domain knowlegde, something AI tools cannot see.
The solution is to govern what AI produces. In practice, that means measuring quality continuously, flagging architectural drift before it reaches production, and prioritizing remediation by business impact.
What matters most here is knowing where your technical debt is, how fast it is growing, and where it hurts your business the most, not bringing it to zero. That is exactly what continuous software portfolio governance delivers.
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Werner Heijstek
Werner Heijstek is the Senior Director at Software Improvement Group and host of the SIGNAL podcast, a monthly show where we turn complex IT topics into business clarity.