Over the past year, a phrase has started circulating in technology circles and across the wider web and social media.
“Vibe coding.“
The idea is very simple. With the help of AI tools, anyone can describe what they want by typing a single sentence, “Build me an app that does XYZ,“ and code starts to appear. Dashboards generate themselves and APIs and databases get stitched together causing entire features to emerge in minutes.
For most people, it feels like magic, and in many ways, it is.
The tools available today give individuals an incredible amount of creative power. A single person can prototype ideas, explore directions, and build working software faster than at any other point in our history.
The AI shift is real, and beneath that acceleration lies a risky trade-off many organizations have not fully considered. Speed and momentum are not the same thing, and confusing the two is where the hidden cost of fast development begins.
Never let the Tools Dictate the Architecture
Most AI coding tools operate within familiar architectural patterns.
✲ They rely on common frameworks.
✲ They generate solutions based on what has worked before.
✲ They assemble software in ways that are statistically likely to function.
For rapid experimentation, this is incredibly powerful.
But when software begins to emerge primarily from what the tooling makes easiest, something subtle happens without you knowing. The tools begin shaping the architecture. Instead of systems being designed deliberately, teams inherit structural decisions through generated code.
At first, nothing appears wrong. The application runs, the feature works, and the demo succeeds. But architecture decisions made implicitly tend to surface later as constraints.
✲ Constraints on scale.
✲ Constraints on integration.
✲ Constraints on how the system can evolve.
What looked like speed and magic at the beginning slowly becomes friction.
How Shortcuts Become Operational Friction
Most technical debt does not come from bad engineers. It comes from reasonable decisions made under time pressure.
✲ A shortcut taken to ship faster.
✲ A library adopted because it solved the immediate problem.
✲ A pattern reused without fully understanding its long-term implications.
Individually, none of these decisions feel significant, but over time they accumulate, and eventually those shortcuts turn into operational friction. I’ve seen teams approve features AI recommended because they were “nice to have.” Over time, simple changes become harder than expected, systems interact in unpredictable ways, and teams spend increasing amounts of time maintaining structure rather than improving it.
The efficiency of your workflow slows down precisely because you tried to move too fast.
The Illusion of Replacement
You see the frightening number of layoffs in the tech industry. Every week there’s an infographic outlining the career fields in danger of AI, and you can’t help but think to yourself, “Damn! That’s rough.”
Recently, a friend of mine was exploring an opportunity to help an organization modernize parts of their back office.
The kind of work many mid-sized organizations eventually face. They have systems that have grown over time and tools that no longer communicate well or with each other. Some processes were digitized, but never truly designed.
During one of the discussions, someone mentioned they had already started building what they needed using one of the new AI coding tools. They described their requirements, it generated the code, and they felt confident it would handle the job.
Then came the comment:
“You’re a software engineer, right? You’re cooked.”
How does one frame or react to this conversation?
“So…I should pay you to build this thing when AI did everything for me in a few hours?” The implication is clear: if AI can generate software, why would organizations still need engineers?
It’s an understandable reaction, but there’s a misunderstanding about where the real complexity of building technology lives. The hardest part of building systems was never typing the code.
It’s designing the structure the code belongs to.
Where Vibe Coding Actually Works
AI-assisted development is incredibly useful in the right context. For small teams, early experiments, and prototypes, it allows ideas to become tangible almost instantly.
You can explore possibilities quickly, validate assumptions, and build things that previously would have taken weeks. In that environment, speed is a superpower, but mid-sized and larger organizations operate differently. They have existing systems, data dependencies, operational workflows and security responsibilities.
Software inside those environments is not just code. It is infrastructure for how the organization operates and it still needs to be designed.
The Difference Between Speed and Momentum
Speed is how quickly you can produce something today.
Momentum is how easily your system allows you to continue moving tomorrow.
✲ A well-designed system builds momentum.
✲ It allows new capabilities to integrate cleanly.
✲ It supports growth without constant rework.
A system built only for immediate speed behaves differently by moving quickly at first before becoming too complex. Eventually, the same system that once felt fast begins to resist change, and acceleration slowly becomes drag.
Experience is still King
Yes, AI can generate code, help debug problems and accelerate how quickly ideas become prototypes.
Tools cannot replace experience. Real expertise comes from solving problems repeatedly over time, especially in high-pressured, critical situations. It comes from observing how systems behave under pressure and developing the discipline to break down large problems into recognizable tasks. It’s about asking the right questions at the right time before any action is taken.
Experience also comes from passion.
Who would you want to be your doctor?
The one who chose the profession simply because it was a great way to make money, or the doctor whose grandfather died when they were young because there were no doctors in their village, and if there had been, grandpa might still be alive today.
The same principle applies in technology.
The best technologists are not just people who can generate code. They are people who genuinely care about how systems work, how organizations operate, and how the technology they build affects the people using it.
This mindset matters because, at the core of any successful system, is the ability to care about the context of the problem and the people experiencing it. People stop caring when the shit doesn’t work.
When something breaks, when systems need to scale, or when security becomes critical, the organization still needs people who understand what is actually happening under the hood. AI tools are powerful, but power without expertise can create fragile systems.
Especially now, in a time when cybersecurity threats and system vulnerabilities are increasingly sophisticated.
Bad actors have just as much power as my 13 year old niece building a website to sell her friendship bracelets online.
The same tools we use to build faster are also helping black-hats generate exploits, automate vulnerabilities, and scale cyber attacks in ways that were previously difficult.
Here’s The Real Trade-Off
AI has dramatically increased how fast technology can be produced. Today almost anyone can generate an app, platform, website, or tool within hours. The result is a landscape filled with products that look the same.
The same patterns.
The same tools.
The same frameworks arranged slightly differently.
You can usually tell when something was built this way. It works, but it feels familiar. Predictable. Lacking depth, intention, and uniqueness. In a market already saturated with text-to-code products, repetition becomes a disadvantage.
Standing out requires more than speed. It requires thoughtful design, original thinking, and a clear understanding of the problem being solved. AI can accelerate the building process, but it cannot replace the judgment required to build something meaningful.
Because in the end, the real advantage doesn’t belong to the teams that simply build faster, it belongs to the teams that build with intention, guided by a clear plan and thoughtful vision.