I’ve been in this game long enough to remember when Delphi came with a promise to embrace everyone to write their own programs. I recall when all visual “website builders” would kill web development. I’ve heard the same pitch when no-code/low-code tools were gaining momentum. Now, standing here in 2025, I’m watching the latest "miracle": Vibe Coding. The pitch is intoxicating. You sit in front of a sleek IDE (or console if you want to feel fancier), you describe a feature in plain English (or other language), and you watch the code miraculously appearing in 100’s of lines per minute. You aren't writing code anymore, nor doing any "engineering" in the traditional sense - you’re steering a vibe. And for a moment, it feels like magic. The velocity is vertical. You’re shipping features before the new JS framework has been announced.
But as a seasoned engineer who has spent countless hours cleaning up "miracles," I have a clear warning: the bill will come due. We are currently living through the "free" phase of AI-generated code, but technical debt has never been free, and it never will be. The current debt is more like a short-term, high-interest loan, and most teams aren't ready—or willing—to repay it after racing ahead at breakneck speed.
Speed vs. Skill
The industry suddenly has a 700-horsepower Italian supercar for $20 a month. In the hands of a professional, it's a marvel—a driver quickly learns its limits and pushes it with skill, mastering physics with precision rather than relying on "vibe."
Now picture a golf cart driver in the same supercar. On a straight, they floor it and feel invincible, trusting the smart safety features. But at the first real corner, the illusion ends—inevitably, the car crashes.
My warning is this: AI coding tools are that supercar. If you’re a seasoned engineer who understands the underlying memory management, database locking strategies, and the nuances of distributed systems, these tools are incredible. But if you’re "vibe coding" without fundamentals, you aren’t in control—you are heading straight for a crash. This risk multiplies if your work affects production systems or customers, not just your own sandbox.
Comprehension Debt
I recall a day when I had to convince C-suite executives that, during a high-growth phase, we had accumulated technical debt and that, even if we doubled the team's size, we would not be able to double output. That was the old days, when technical debt was “human-made”. We took shortcuts, and we knew where the bodies were buried - because we buried them.
Now the AI-induced debt is different. It’s what I call Comprehension Debt. When an LLM generates 500 lines of code for a complex feature request, and you "accept" it because it passes the happy-path test (or vibe test), you have just introduced a block of logic into your system that no human fully understands (aka AI slop). When I’m meeting with my tech fellow, I hear that while development speed has increased by 30-40% across many teams, maintenance costs have spiked.
Why? Because when that code breaks at 3:00 AM on a Sunday, the following happens:
Zero Intentionality: The AI lacked a rationale; it had only a probability distribution. It didn't "decide" to use a specific pattern because it anticipated future scaling; it used it because it was common in its training data.
Context Fragmentation: The AI sees a window of code - a "context window." It doesn't know your database has a specific quirk with concurrent writes to handle Black Friday traffic, nor does it know that your storage bucket has a specific lifecycle policy that will delete the files it’s trying to reference.
The Hallucination of Robustness: AI code often looks nice. Perfect naming conventions, includes comments, and sometimes uses a “smart” approach to solve a problem. Your vibes are lured into a false sense of security as code appears to be the work of a senior developer. The tool that you hold in your hand is not a Cyberpunk surgical robot; it’s a big hammer that looks like a surgical tool.
Big Hammer and the Broken Fingers
Vibe coding is a massive hammer. In skilled hands, it’s the most efficient tool ever created (”It takes a hammer to get the job done”). Do you know how to use a hammer? At the end of the day, you are holding a nail with one hand and a hammer in the other. Are you skilled enough not to break all your fingers?
I’ve never seen in my career a software engineer whose only purpose was to spit out lines of code as fast as possible to solve a problem with the highest probability of success. It was always a mix of responsibilities, starting with clarifying specifications and moving into system engineering, everything bounded by context - usually undocumented. Writing code has always been the easiest part of the process. Fundamentals had not changed; they are the only thing standing between a functioning product and a catastrophic failure. I’m talking about:
Corner Cases: Be warned - AI is good at the average case, but often fails at unique edge cases, such as race conditions, network timeouts, or full disk scenarios. Overlooking these can cause critical failures. You delivered your personal CRM in a week of vibe coding, and it’s perfectly suited to your needs. Hey, maybe your use cases are not so unique after all. But you saved $12 a month on your “old” CRM subscription.
System Boundaries & Execution Environments
An LLM can write a perfect Python script, but it doesn't know it's running in a memory-constrained Lambda function. It doesn't know that the "vibe" it just generated will trigger an O(n2) complexity spike that times out the gateway.
For example, consider a simple data transformation. The AI might suggest:
T(n)=O(n2)
Because it looks "clean" in the code editor. A seasoned engineer sees the number growing to 1,000,000 in production and knows that the "vibe" is actually a ticking time bomb.
Context Management: An AI sees a window of code (sometimes it’s even just one file). An engineer sees a living ecosystem. The AI doesn't know your cloud bill is about to quadruple, even though it suggested a recursive Lambda call.
The 2027 Wake-Up Call
I predict that over the next two years, any company that ignores tech debt will face a major “Great Refactoring” crisis - though they likely won’t admit it. Teams that replaced senior oversight with "vibe-heavy" approaches will become trapped by fragile, unmanageable codebases. The risk is real: these systems could become too complex and costly to repair or change.
We are already seeing the signs. Pull request review times have tripled in some organizations because reviewers now have to sift through mountains of "plausible-looking" but subtly broken AI code. Open source projects are blocking PR requests (after decades of asking for contributions). This shift is meaningful. Open source (and communities around it) is the heart of the IT ecosystem, and it’s the key (underpaid) driver of IT growth in the last 30 years. We are one of the most open industries, where not only knowledge is shared for free but also the results of the work (Python, Ruby, Linux, Git, etc.). Yet we are closing the gates to AI, not curated, slop being poured into the fundamentals of the industry.
Craftsmanship in the Age of AI
Am I anti-AI? Not at all. I use a wide set of these tools every day. But I use them with the healthy skepticism of a man who has seen too many "magic" solutions turn into technical nightmares.
True craftsmanship in 2026 isn't about how fast you can type or how well you can prompt (even though every AI company will tell you a different story). It’s about:
The Rigorous Review: Treating AI code with more suspicion than human code. It’s your phone number that will ring at 3 AM if your app goes down.
Defensive Architecture: Building systems that are modular enough that when an AI-generated component fails, it doesn't take down the whole ship. Where the context is small enough for an LLM to handle.
Fundamental Literacy: Knowing enough about the "boring" stuff - protocols, data structures, and memory - to spot the lie in the AI’s beautiful output.
The era of "free" tech debt is coming to an end. My final warning: the reality of production is returning. Don't discard your engineering books - you'll need them to repair what "vibe coding" leaves behind. Don’’t replace your engineering team with agents (workflows).
“Look Right” Audit
As a seasoned engineer, I’ve realized that the most dangerous part of "vibe coding" isn't the AI - it's the reviewer's competency. When code is generated in seconds, our brains naturally want to review it in the same amount of time. We "skim the vibe" instead of "auditing the logic". We are not spitting out 500 LOC per 10 minutes to spend 3 hours to understand - don’t we? How do you justify to your manager that you have your feature done, it looks alright, but you would like to understand the code you will push to production soon? No, you are becoming a bottleneck, that’s perceived by the manager with a whip.
To survive the next two years of high-interest tech debt, we have to treat AI-generated code with higher suspicion than a junior dev’s PR. Here is my personal "AI checklist":
Context & Intent
Why Alignment: Does this PR actually solve the business requirement, or did the AI solve a similar problem it found in its training data?
Scope Creep (”While I'm At It" Trap): Did the AI refactor or change logic in files unrelated to the ticket? LLMs love to "clean up" things they don't understand, often breaking hidden invariants.
Domain Accuracy: Does the code use our specific domain terminology (e.g., SaaS_Subscription) or generic AI terms (e.g., User_Level)?
System Logic
"N+1" and Complexity Check: Did the AI suggest a clean-looking loop that secretly makes 100 database calls or has an O(n2) complexity on a high-volume path? Is there a long if/else statement?
State & Race Conditions: If this code runs in a distributed environment, are there locks? Is the state handled safely? (AI is notoriously "single-threaded" in its reasoning).
"Empty Room" Test: What happens if the input is null, an empty string, or an array of 1,000,000 items? AI often ignores the 1% edge cases, and it will happen on Friday evening.
Dependency Hallucination: Are all imports real, internal libraries? Or did the AI "invent" a utility function that looks like it should exist? Unused imports removed? What about variables or assumed configuration values? Are you reusing internal dependencies, or does every PR create a new one that does the same thing?
Config Check: The local environment (where development happens) often differs from the production environment. Can code generated from a local config be used in production?
The Infrastructure & Security Audit
Resource Cleanup: Are database connections, file handles, or memory buffers explicitly closed?
The Secret Leak: Did the AI suggest hardcoding a "placeholder" API key or an insecure http:// endpoint? Is it using the same security patterns as you have in other parts of the application?
Input Sanitization: Is there a raw SQL string or an unvalidated user input being passed to an OS command? AI prioritizes "working code" over "secure code" by default.
Conclusion: Craftsmanship is the New Premium.
Am I anti-AI? Absolutely not. I am enthusiastic about the fact that I can now prototype an idea in an afternoon. These tools are the most significant leap in productivity since the invention of the high-level language.
But we must stop pretending that the tool replaces the craftsman.
True craftsmanship in 2026 isn't about how fast you can type or how well you can prompt. It’s about judgment. It’s about knowing when to say "No" to the AI’s suggestion. It’s about understanding the database structure, the execution environment, and the corner cases so well that you can spot the one line of "vibe" that will take down the entire system.
The era of "free" tech debt is ending. The "vibes" are fading, and the harsh light of production reality is turning back on. If you want to keep your fingers intact, stop blindly swinging the big hammer. Put on your seatbelt, learn the physics of the car, and remember:
Code is easy, engineering is hard.
