🔆 Shortcuts and Side Effects: AI’s Hidden Tech Debt

🗞️ Issue 67 // ⏱️ Read Time: 5 min

Hello 👋

AI is transforming businesses fast, but it’s also creating a hidden challenge: technical debt. Companies rushing AI adoption often take shortcuts, causing problems down the road. This debt can slow innovation and hinder maintenance if not managed carefully. Let's explore what it means, why it matters, and how to deal with it.

What is Technical Debt?

Technical debt (a.k.a tech debt) is like taking a shortcut to finish a project faster, knowing you’ll have to fix or improve things later. In software, tech debt happens when teams choose quick fixes or simpler solutions to meet deadlines or launch products faster, instead of building things the best way from the start. And we all know the tech industry likes to “move fast and break things.” 

Sometimes, taking on some debt helps achieve urgent goals. But, just like financial debt, tech debt “accrues interest.” Left unaddressed, it takes more time and resources to fix, slowing future progress.

How AI Changes Things

AI is promoted as a way to get quick results, with tools that promise to build software or automate tasks at the click of a button - another tempting shortcut. We got into the details of this in our previous newsletter on vibe coding. While saving time and increasing access, these solutions introduce risks. 

When organizations rely on AI-powered tools or no-code platforms to build complex systems, they may not fully understand how those systems work under the hood. This lack of understanding can be dangerous: some companies have faced costly outages or security breaches when an AI-powered system failed and no one on the team had the skills to fix it.

Even worse, technical debt can quietly pile up as automated decisions and shortcuts accumulate in ways no one notices until it’s too late. Organizations can end up dependent on systems they can’t control or maintain, making future changes costly and risky.

Tech Debt is on the Rise

The evidence is clear: technical debt is rising as organizations rush to implement AI solutions. Forrester predicts a “technical debt tsunami” in 2025 and beyond, with more than half of technology leaders already reporting moderate to high levels of tech debt, a figure expected to reach 75% by 2026.

Several factors are driving this trend:

• Complexity and Fragmentation: AI projects often require integrating new models into legacy systems, increasing IT landscape complexity and the risk of accumulating debt.

• Speed Over Stability: The pressure to deploy AI quickly can lead to shortcuts in design, documentation, and testing, compounding future maintenance costs.

• Ongoing Maintenance: AI models must be regularly updated to correct for bias and to adapt to concept drift, which occurs when real-world data evolves and the model’s predictions become less reliable. This ongoing need for adjustment and oversight adds new challenges to managing technical debt.

At this point, AI is one of the highest contributors to tech debt.

"Perfect" Architecture vs. Shipping Value

While managing tech debt is important, there's a compelling argument that too much focus on "doing things right" can be equally problematic, especially for startups and innovation-focused teams.

A startup advisor recently shared on LinkedIn how a founder came to him after their $5.3M-funded company nearly ran out of runway. The CTO had spent 22 months building “the right way,” with perfect architecture rebuilt for every product pivot, while the market kept changing.

The key: iterate scrappy, but don’t scale scrappy. Quick, imperfect solutions help you pivot and find product-market fit, but once it’s time to scale, investing in solid foundations is essential. Balance speed with regular improvements and clear triggers for when to strengthen your tech so that debt never gets out of hand.

Tackling Tech Debt Proactively

Zero technical debt is not a realistic goal, but managing it well is possible. Proactive organizations regularly update their systems, keep data organized, and set aside budget for ongoing improvements, treating tech debt as a strategic business issue, not just a technical one.

Using AI to Reduce Tech Debt

Luckily, AI isn’t just part of the problem, it’s also part of the solution. Here are ways AI tackles the challenge:

1. Smarter Code Analysis: AI can spot hidden patterns of technical debt, like duplicated logic or overly complex code, faster and more thoroughly than traditional tools, helping teams target fixes where they matter most.

2. Automated Refactoring Assistance: Refactoring means cleaning up and improving existing code without changing what it does. AI-powered tools can suggest and even make these improvements automatically, streamlining cleanup and reducing manual effort.

3. Understanding Legacy Code: Legacy code is old software that’s still in use but often poorly documented or written in outdated languages. AI can analyze, document, and even help modernize these systems, making it easier to tackle long-standing debt.

Ultimately, successfully harnessing AI's power requires a deliberate approach to technical debt. Balancing the need for speed with a commitment to robust, maintainable systems is the key to unlocking long-term value. Striking the right balance isn’t a one-time effort, it’s a continuous process that demands discipline as your organization grows.

Until next time.
On behalf of Team Lumiera

Emma - Business Strategist
Allegra - Data Specialist

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