What Every Experienced CTO Wishes They’d Done Differently About Technical Debt
Huzefa Motiwala April 10, 2026
Technical debt is inevitable in software development, but managing it poorly can cripple your team’s productivity and your business’s growth. Here’s what experienced CTOs wish they had done differently:
- Not all technical debt is bad: Some debt accelerates progress if repaid later, while unmanaged debt can consume 40% of engineering resources.
- Track and prioritize debt: Tools like a "Traffic Light Roadmap" help classify debt as Critical (fix now), Managed (plan to fix), or Scale-Ready (monitor).
- Tie repayment to growth: Align fixes with milestones like product launches or compliance deadlines.
- Communicate in business terms: Explain debt as a "tax" on future development, linking it to delays, costs, or lost revenue.
- Use AI for early detection: AI tools can identify problem areas like complex code or outdated dependencies before they become crises.
The goal isn’t to eliminate technical debt but to manage it wisely. This article dives into strategies that CTOs use to balance speed, stability, and scalability.
Finding Technical Debt Before It Becomes a Crisis
What Happens When Debt Goes Undetected
Technical debt often lurks unnoticed until it causes major issues like production failures, fragile deployments, or developers hesitating to modify code. Studies reveal that 30–45% of engineering time is wasted on tasks that don’t directly add customer value[6]. Alarming signs of technical debt include project timelines exceeding estimates by 50–100%, small changes impacting 12 or more files, or over 30% of module updates leading to incidents[6].
Take the example of a fintech startup: 38% of failed deployments were tied to a single outdated payment module[3]. Ignoring such warning signs can be costly – fixing problems like missing database migrations or hard-coded API keys can run anywhere from $15,000 to $40,000[4].
How AI Can Help Detect Debt
AI tools are becoming indispensable for finding technical debt early. These systems can scan codebases and highlight architectural problems before they spiral out of control. They flag issues like high cyclomatic complexity, duplicated code, and areas with insufficient test coverage – all of which are common indicators of debt[10].
"Technical debt isn’t ‘bad code.’ It’s the delta between the current state of your system and the state it needs to be in to support your current and near-future business goals." – Victor Quinn, Co-founder and CTO, Texture[6]
One example is AlterSquare’s AI-Agent Assessment, which evaluates codebases for issues like architectural coupling, security vulnerabilities, and performance bottlenecks. It then provides a Traffic Light Roadmap to classify problems as Critical (fix immediately), Managed (schedule for later), or Scale-Ready (monitor and maintain)[10]. AI can also suggest quick fixes during pull requests, addressing issues like missing null checks or outdated dependencies without slowing development. For larger refactors, AI breaks complex changes into manageable, reviewable steps, keeping the process steady and efficient[10].
This kind of proactive approach lays the groundwork for measurable results, as the following case study illustrates.
Case Study: Catching Debt Before Scaling
Early detection of technical debt proved transformative for Coalescent (now Dapian). In January 2025, Jonny Coombes stepped in as CTO and discovered five years’ worth of accumulated debt, including a heavy reliance on AWS. By May 1, 2025, his team completed "Project Cuban Pete", migrating to a Kubernetes-based architecture and replacing AWS dependencies with Rust-based microservices called Oryx. This shift not only avoided downtime for customers but also restored the company’s technical independence[9].
The team’s success hinged on identifying and quantifying debt early. They analyzed delivery impact, change failure rates, and dependency freshness to prioritize their efforts. Using git logs, they pinpointed files with frequent changes and bug-fix commits – clear signs of debt slowing progress[8]. Addressing these issues before scaling saved them from higher costs and risks. By tackling the debt systematically, they ensured smooth feature development and upheld customer trust.
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Technical Debt & other Hidden Costs
Good Debt vs. Bad Debt: Knowing the Difference
Technical Debt Classification Framework: Traffic Light Roadmap for CTOs
When Technical Debt Is Worth Taking On
Not all technical debt is bad. In fact, some of it is a calculated choice – a trade-off that helps a business move faster, provided there’s a solid plan to repay it later. Victor Quinn, CTO of Texture, explains that technical debt can be a powerful tool to speed up progress when managed responsibly [2].
The trick lies in identifying safe shortcuts. At Texture, Quinn opted for RedwoodJS and Tailwind UI to quickly deliver an initial dashboard, knowing the UI wasn’t a critical element at that stage. Later, when the dashboard became the main customer interface, the team addressed the debt by rebuilding it with a proper design system. Similarly, Texture initially used Render for infrastructure instead of AWS, which allowed them to operate without a dedicated DevOps engineer. However, when enterprise customers demanded SOC 2 compliance, the company transitioned to AWS to avoid a massive increase in manual audit costs.
Good technical debt typically involves non-core components like UI frameworks, admin tools, or internal workflows. It’s also tracked using TODO comments or a debt register to ensure repayment isn’t forgotten. Interestingly, startups that actively manage their technical debt have a 60.6% funding success rate, compared to 44.4% for those who focus solely on maintaining pristine, but slower, codebases [1].
When Technical Debt Becomes a Problem
On the flip side, bad technical debt arises when shortcuts compromise the stability of your system or when debt accumulates without intention. This includes issues like defect debt due to inadequate testing, architectural debt from poor design choices, and, as Victor Quinn describes, damage to "load-bearing beams" – core elements like data models, authentication systems, or critical subsystems such as financial ledgers [2].
"If you have zero technical debt, you are probably moving too slow." – Ewelina Lech, Pragmatic Coders [1]
Signs that technical debt is spiraling out of control include teams spending over 20% of their time fixing bugs, project deadlines being missed by 50–100%, or developers avoiding certain parts of the codebase out of fear. Left unchecked, bad debt can sap up to 40% of a company’s technology value and engineering capacity, and in extreme cases, it can lead to disastrous outcomes.
The real danger arises when teams continue building on top of bad debt, allowing it to silently grow. Without clear boundaries or interfaces to contain it, this debt can spread throughout the codebase like a hidden tax on productivity [7]. Recognizing these warning signs is the first step toward tackling the problem effectively.
How to Classify Your Debt
To manage technical debt effectively, it’s essential to distinguish between shortcuts that help and those that hurt. The Traffic Light Roadmap offers a simple yet actionable framework for categorizing debt into three levels based on its business impact and the risk involved in addressing it:
| Priority Level | Characteristics | Action |
|---|---|---|
| Critical (Red) | High impact, low risk; affects essential systems like data models, authentication, or financial ledgers | Do Now: Dedicate 2–4 sprint hours to address these issues immediately |
| Managed (Yellow) | High impact, high risk; requires careful planning and stakeholder alignment | Plan: Develop a refactor plan through an RFC (Request for Comments) |
| Scale-Ready (Green) | Low impact; isolated legacy systems that are stable but outdated | Contain: Add guardrails like tests or interfaces; monitor but avoid rewriting for now |
To apply this framework, evaluate each piece of debt by asking whether it was a deliberate or accidental choice and whether it was taken recklessly or prudently [14]. Reckless, deliberate shortcuts – those made without any plan – should be your top priority, as they often point to deeper process flaws.
It’s also crucial to set clear triggers for repayment. For example, if a small change takes twice as long as expected or if debt blocks compliance with enterprise requirements, it’s time to act. As a general guideline, reserve 20–30% of your sprint capacity for addressing technical debt and maintaining code quality to keep your development velocity intact [14].
Timing Debt Repayment with Business Milestones
Repayment Strategies for Each Growth Stage
Once you’ve identified and categorized technical debt, the next step is to align its repayment with your company’s growth milestones. CTOs often tie debt repayment to pivotal events like product launches, revenue goals, or compliance deadlines, ensuring these efforts directly support business growth.
At the Validation stage, adopting a Disposable Architecture approach can be highly effective. This means implementing temporary solutions that can be replaced once the market fit is confirmed. Taking shortcuts is acceptable at this stage, as the focus is on testing viability. For example, in 2024–2025, Texture’s CTO, Victor Quinn, used this strategy by launching their initial dashboard with RedwoodJS and Tailwind UI to move quickly. Once the dashboard became a key customer interface and the business model was validated, they rebuilt it using a more robust design system [2].
During the Growth stage, the focus shifts to Managed Refactoring, where 15–25% of each sprint is allocated to resolving bottlenecks [3]. Texture provides a great example here: they relied on Render for 18 months to maintain productivity with a solo developer. When enterprise customers required SOC 2 compliance, they planned and executed a migration to AWS – a repayment tied to a critical business milestone [2].
At the Scale stage, Strategic Modernization becomes essential. This involves modularizing monoliths, strengthening infrastructure, and ensuring compliance. At this point, new debt should be minimized, and efforts should focus on foundational systems that, if neglected, could hinder enterprise growth or readiness for an acquisition.
Good vs. Bad Debt at Different Stages
The definition of good versus bad technical debt evolves as your company grows. What might be acceptable during the early stages can become a liability at scale.
| Stage | Good Debt (Leverage) | Bad Debt (Foundation Risk) | Mitigation Strategy |
|---|---|---|---|
| Validation | Using a simple PaaS like Render to ship independently [2]. | Building a fintech product without a proper ledger [2]. | Disposable Architecture: Assume parts are temporary and plan to rebuild upon success [7]. |
| Growth | Manual internal workflows to test feature demand [2]. | Skipping unit tests to meet deadlines without a payback plan [14]. | Managed Refactoring: Dedicate 20% of sprint capacity to debt and target the most problematic areas [5][11]. |
| Scale | Documented shortcuts with clear remediation triggers [6]. | Reckless debt caused by lack of mentorship [14]. | Quarterly Reviews: Use "Debt Registers" to align repayment with business objectives [6][12]. |
By syncing repayment strategies with growth stages, you maintain system stability and efficiency over the long term. Conducting regular reviews ensures these approaches remain relevant as business priorities evolve.
Making Debt Repayment Part of Regular Reviews
To keep technical debt under control, CTOs often schedule regular reviews. Quarterly Principal Council reviews, which involve senior engineering leadership, are crucial for evaluating debt items based on their impact on the business, urgency, and the effort required for resolution [5][13].
These reviews should revisit the debt register to account for shifting priorities. For instance, debt labeled as "Scale-Ready" six months ago might now be "Critical" if you’re targeting enterprise clients or preparing for an acquisition. Architecture Decision Records (ADRs) can be a valuable tool for documenting why certain shortcuts were taken and setting clear triggers for repayment. For example, an ADR might state, "Migrate to WebSockets when monthly active users exceed 7,500" [6].
"Technical debt is only a problem when it impacts business value. Prioritizing debt that slows development, increases defects, or introduces risk is what separates effective CTOs from the rest." – Sri Laxmi, AI Product Manager [5]
Communicating Technical Debt to Non-Technical Stakeholders
Effectively explaining technical debt to non-technical stakeholders is essential for aligning engineering priorities with broader business objectives. This is a key takeaway many seasoned CTOs emphasize.
Using the Traffic Light Roadmap
To bridge the gap between engineering concerns and business priorities, CTOs can use the Traffic Light Roadmap. This framework translates technical debt into clear business risks using three visual categories: Critical (red), Managed (yellow), and Scale-Ready (green).
- Critical (Red): Represents debt that jeopardizes system stability.
- Managed (Yellow): Refers to debt that slows progress but isn’t immediately harmful.
- Scale-Ready (Green): Covers intentional shortcuts that will be addressed before scaling.
This approach makes it easier for stakeholders to grasp how technical risks align with their goals. For example:
- CEOs see red as a threat to production stability and responsiveness in the market.
- CFOs view red as financial risk tied to potential system failures.
- Product managers recognize red as delays in delivering features.
By framing technical debt in this way, engineers can translate their concerns into terms that resonate with business priorities. This sets the stage for discussing how these risks impact measurable outcomes.
Translating Debt into Business Terms
To make technical debt relatable, describe it as a "tax" on future development that impacts key business metrics. Industry estimates suggest that technical debt can waste about 25% of developers’ time. For a team of 10 engineers earning $150,000 annually, this equates to $375,000 in lost productivity every year [17].
Here’s how technical concepts can be reframed for business stakeholders:
| Technical Term | Business Translation | Measurable Impact |
|---|---|---|
| Refactoring | Reducing deployment risk | Lower incident costs; improved stability |
| Code quality | Time-to-market | Faster feature delivery; shorter lead time |
| Legacy code | Maintenance tax | Higher total cost of ownership (TCO) |
| Technical debt | Strategic leverage (if deliberate) | Improved speed-to-market and NPV |
For example, instead of saying, "Our API needs refactoring", explain it in business terms: "Delivering the customer dashboard feature will take six weeks instead of two because our API debt requires extra workarounds for every new endpoint" [15]. This shifts the focus to tangible impacts like delays and costs.
Getting Buy-In for Debt Repayment
Position debt repayment as a smart investment with clear ROI. Calculate the "payback period" by comparing the cost of addressing the debt to the ongoing "interest" paid through reduced velocity and higher incident rates.
For instance, Adobe’s 2026 refactor of a critical component cut customer support tickets by 40% [16]. Similarly, Shopify’s checkout system overhaul reduced page load times by 65%, which led to a 27% increase in conversion rates [16]. These examples show how resolving technical debt can directly improve revenue and customer satisfaction.
"Tech debt is a tax on future development. Quantify the tax. Show the tax applies to work the stakeholder already cares about." – Sanjeev Sharma, Full Stack Engineer [15]
When pitching debt repayment, tie it to specific business goals. For example:
- If targeting enterprise clients, explain how fixing the authentication system enables SSO and compliance.
- For international expansion, highlight how addressing hardcoded US-centric logic supports multi-currency and localization needs [16].
This approach reframes debt repayment from a technical concern into a business-critical investment that drives growth and efficiency.
What CTOs Wish They’d Known: Key Lessons and Next Steps
Main Lessons from Experienced CTOs
Successful CTOs often view technical debt as a calculated tool. Skipping foundational elements like core data models, security, authentication, or financial ledgers can lead to skyrocketing costs down the line. However, taking smart shortcuts in less critical areas – like UI frameworks or admin tools – can speed up delivery without major risks [2].
Visibility is key; untracked debt can spiral into a hidden problem that creates political and operational challenges. To stay in control, experienced CTOs document every shortcut they take. Tools like Architecture Decision Records (ADRs) or a Debt Register help track the location, severity, and potential business impact of each decision [6][3]. Additionally, they plan to rebuild core components every 18–24 months to adapt to evolving product needs and market demands, avoiding the trap of clinging to outdated systems [2].
Managing technical debt effectively is essential as a company grows. Early-stage startups often use debt aggressively to learn quickly, while scaling companies dedicate 15–25% of each sprint to repaying it. At the enterprise level, minimizing new debt becomes the focus to ensure long-term stability [1][7]. Interestingly, startups that track and manage debt strategically see a funding success rate of 60.6%, compared to 44.4% for those with slower but cleaner codebases [1].
These lessons highlight the importance of strategic oversight in technology management. Specialized tools can make this process even smoother, building on frameworks like the Traffic Light Roadmap to ensure debt is managed proactively through automated assessments and adaptable strategies.
How AlterSquare Can Help
AlterSquare offers tools and frameworks designed to help CTOs stay ahead of technical challenges. Their AI-Agent Assessment scans your codebase to pinpoint architectural coupling, security vulnerabilities, performance issues, and tech debt hotspots. These insights help inform critical decisions before they escalate. The Principal Council (Taher, Huzefa, Aliasgar, and Rohan) then applies business context to deliver a Traffic Light Roadmap, which categorizes issues as Critical (red), Managed (yellow), or Scale-Ready (green). This service starts at $2,500.
For teams ready to act, AlterSquare’s Variable-Velocity Engine (V2E) framework adapts to your company’s growth stage. It transitions from a Disposable Architecture designed to achieve quick revenue, to Managed Refactoring that supports reliable scaling, and finally to Governance & Efficiency for preparing the business for exit. This founder-led approach ensures structured handoffs and cross-trained teams, so critical knowledge and context are never lost.
FAQs
How do I decide if a shortcut is “good debt” or “bad debt”?
To figure out whether a shortcut qualifies as good debt or bad debt, think about its purpose and the long-term effects. Good debt involves intentional decisions, like taking a shortcut to test an idea, paired with a solid plan to address it later. On the other hand, bad debt comes from careless shortcuts taken without a strategy, which often result in expensive complications down the road. The trick is to ensure that any shortcut aligns with your business objectives and keeps future costs under control.
What signals show technical debt is starting to slow the team down?
Technical debt might be dragging your team down if you’re seeing more daily struggles, slower progress on projects, or dwindling morale caused by lingering or overlooked problems. These red flags often suggest that unresolved debt is stalling efficiency and causing roadblocks.
How much sprint capacity should we reserve for paying down tech debt?
To keep a healthy balance between building new features and tackling technical debt, it’s a good idea to dedicate about 20% of your sprint capacity to resolving tech debt. This strategy helps ensure long-term stability while keeping your development speed on track.
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