There is a famous line from the movie Field of Dreams:
“If you build it, they will come.”
In 2025, for tech teams rushing to embrace AI coding, the new version sounds more like: “If you build it too fast, technical debt will come—and it might stay longer than you think.”
The explosion of AI code generation tools has made it easier than ever to launch new features, spin up prototypes, and ship faster.
But speed without structure comes with a price. That price is technical debt.
Before we go deeper, it is important to be clear: technical debt isn’t always a bad thing.
The term was first coined by Ward Cunningham, one of the original authors of the Agile Manifesto.
He used it as a metaphor to describe the tradeoff between short-term speed and long-term sustainability in software development.
In fast-moving environments, taking on some technical debt deliberately can be smart.
It helps companies release products faster, validate ideas early, and respond to market shifts.
The real danger comes when debt is unmanaged, hidden, or simply ignored.
At Inclusion Cloud, we recently wrote about how AI is amplifying the best developers—not replacing them.
But AI also has a hidden effect:
It can accelerate technical debt faster than traditional development ever could, especially when teams are not intentional about how AI is integrated into their engineering workflows.
And that is the critical shift technology leaders must face now.
Technical Debt Is Growing, and AI Is Fueling the Fire
Forrester predicts that by 2025, more than 50% of technology decision-makers will face moderate to severe technical debt. That number is expected to hit 75% by 2026.
And it is not just legacy systems causing the problem.
It is how teams are using AI without embedding it into a disciplined engineering framework, one led by senior, certified developers who combine architectural thinking with strategic prompting.
Generative AI is speeding up code generation, but in many cases, it is also:
- Producing code without aligning to architecture principles or system design patterns.
- Duplicating logic instead of reinforcing modularity and pattern reuse.
- Skipping documentation, meaningful naming conventions, and robust testing.
- Creating a dangerous feedback loop where “it runs” becomes the new quality standard.
In short, AI can enable you to build faster.
But it can also enable you to build faster into a corner.
It is easy to fall into the illusion that more features mean more progress.
As Steve Jobs once said, “Simple can be harder than complex. You have to work hard to get your thinking clean to make it simple.”
With AI pushing rapid development, that discipline becomes non-negotiable.
When vibe coding turns into vibe architecture
In our last newsletter episode, we warned about the rise of vibe coding.
In AI-driven environments, a new risk is emerging: vibe architecture.
When quick prompts, half-understood APIs, and fragmented microservices pile up without orchestration, the system itself starts to lose cohesion.
Small inconsistencies drift into large-scale architectural entropy.
You start seeing:
- Fragile integrations that break at the edges.
- Silent blast radiuses where small bugs cascade into outages.
- Data pipelines riddled with inconsistency and duplication.
- Feature bloat without system-wide optimization.
The story of Field of Dreams is simple: build with focus.
In AI-driven development, the same rule applies.
Without strong engineering principles baked into the process from the beginning, even the most powerful AI will accelerate drift instead of delivering stability.
The stats are sounding the alarm
New data confirms this trend:
- In 2024, GitClear found an 8x increase in large blocks of duplicated code generated by AI tools.
- 46% of code changes were entirely new lines, while refactored (moved) code dropped significantly—meaning developers reused less and duplicated more.
- The State of Software Delivery 2025 report shows most developers now spend more time debugging AI-generated code and resolving security vulnerabilities than before.
- Google’s 2024 DORA report found that a 25% increase in AI usage leads to a 7.2% decrease in delivery stability.
Technical Debt in 2025: Not Just Slower, but Riskier
In traditional development, technical debt slowed teams down over time.
Today, technical debt creates fragility that can be catastrophic at scale.
1) It makes AI tools less effective inside your own systems.
2) It clogs your pipelines with hard-to-maintain code.
3) It forces expensive rework when you should be innovating.
The bigger problem is that AI adoption magnifies the gap between low-debt and high-debt environments.
Clean, modular, well-documented systems let AI become a supercharger.
Tangled, patchworked systems suffocate AI’s value, and eventually suffocate the business trying to run them.
Why senior developers are now the center of gravity
This is why AI-augmented senior developers are becoming the real power players.
They are not faster because they type faster.
They are faster because they can:
- Architect resilient systems.
- Refactor AI-generated chaos into clean, scalable modules.
- Build observability, testing, and security into the foundation.
- Guide AI tools strategically, not blindly.
Without senior oversight, AI turns development into a game of tech debt roulette.
You might get lucky for a while.
But you cannot outrun drift and entropy forever.
Technical debt grows in silence.
Experienced engineers are the ones who put it under a microscope — spotting issues before they metastasize across your systems.
Certified senior developers bring the architecture mindset that AI lacks.
They validate outputs rigorously, catch shallow fixes before they pile up, and turn AI into a disciplined extension of the team rather than a loose cannon.
Without them, your velocity is just borrowed time.
The new playbook for surviving and thriving with AI
If you want to win the next chapter of software development, it is not just about adding AI to your toolchain.
It is about changing how you build.
- Treat AI suggestions like junior developer code.
Review, refactor, and question everything.
- Embed technical debt tracking into your delivery pipelines.
You cannot afford to guess anymore.
- Prioritize modular architectures, strict API boundaries, and domain-driven design. That’s how you future-proof your stack (AI or not).
- Build AI literacy across your senior teams.
Prompting is a skill, but so is recognizing when AI gets it wrong.
- Invest in training juniors intentionally.
Teach them to think critically, even when AI makes things easier.
Agile, tech debt, and knowing when to push forward
There’s a hard truth most teams forget:
Agile practices often involve taking on technical debt by design.
It’s like starting to build a house while promising yourself you’ll dig the foundation later.
Sometimes, that gamble pays off—especially early in a product’s life when validating an idea matters more than scaling it.
Other times, debt becomes a noose tightening around your engineering velocity.
No technical debt could mean that your company is not delivering.
If you’re not shipping fast enough to learn, you lose the market.
But if you’re always shipping without fixing, you lose control.
Knowing when to take on debt intentionally, and when to pay it down aggressively, is now one of the most valuable skills in software leadership.
Conclusion: Fast is Only Good if You are Headed in the Right Direction
The future isn’t just about building faster.
It’s about combining the speed AI tools can offer with a clear understanding of their current limitations and strengths. In the hands of senior, certified developers with architectural vision, AI becomes a true productivity accelerator—helping teams avoid fragile engineering, hidden risks, and bad practices that pile up over time.
And here’s something every tech leader should remember:
AI amplifies your technical posture, good or bad.
It doesn’t magically fix it.
- If you build carefully, your systems will scale.
- If you build carelessly, your technical debt will scale even faster.
Otherwise, you’ll be stuck patching a system no AI can save—wasting time, money, and burning out your team.
The last thing you want is to get caught in an endless maintenance loop, draining the energy that could have gone into innovation, new product launches, or the projects stuck in backlog.
The companies succeeding with AI today aren’t just chasing code velocity.
They’re focused on building teams with the right seniority—engineers and developers who can see beyond the next line of code, who think in terms of architecture, and who hold the certifications to prove their deep knowledge of enterprise platforms.
At Inclusion Cloud, we specialize in providing certified talent that accelerates AI initiatives: And we know how hard it’s getting to tell who actually understands architecture—and who’s just copy-pasting from an AI tool.
That’s why we built a strict selection process to find and deliver top certified talent—developers and engineers who don’t just generate code.
They build value.
If you’d like to learn more about our process, reach out here to schedule a meeting.