10.07.26 By Pablo Romeo
Sometimes, people take things to extremes. It happens with ideas, with passions, with habits, and of course, it also happens with AI. It is now happening with how companies build software. Some of them looked at what AI coding tools could do, got excited, and kept going well past the point where excitement should have turned into caution. They went overboard, and the products their entire business depends on were built that way: fast, cheap, and almost entirely unsupervised.
Extremes are rarely healthy, in technology or anywhere else. The companies that pushed hardest are now finding out that their product cannot handle real production conditions, and that the team who built it does not have the training to fix what they built. For example, earlier this year, security researchers at Wiz breached Moltbook’s database and found 1.5 million exposed API tokens and 35,000 email addresses, the direct result of vibe coding without the guardrails a governed environment would have enforced automatically.
A prototype and production-grade software are two very different product stages. AI is excellent at accelerating the first, but if you want a solid product that actually adds value to its users, you cannot stop there.
A coding agent that returns code that runs and produces the expected output has only cleared the lowest bar software has to clear. Solid architecture principles, production-grade infrastructure, security, scalability, error handling (just to name a few “musts”), decide whether that software survives contact with real users.
Those areas are precisely where AI is weakest. Veracode tested more than 100 models and found that 45% of the time, the generated code introduced a known security flaw. While AI-assisted coding has made syntax pass rates climb to roughly 95% since 2023, security pass rates remain flat between 45% and 55%, across every model generation tested. Researchers at Georgia Institute of Technology confirmed more cases of vulnerabilities in AI-generated code in March 2026 alone than in all of 2025 combined.
Security is only one failure mode. An architecture built without foresight cannot absorb the features a growing product will need next, and rewriting a system under pressure usually costs far more than designing it correctly the first time.
Infrastructure carries the same risk in a different form. A product that cannot handle its own traffic will go down, and the standard emergency fix (throwing more cloud resources at the problem) tends to burn through your infrastructure budget fast.
Error handling is another crucial aspect for production-grade software, as it determines what happens after something breaks. Vibe-coded software rarely accounts for error handling. Code that only handles the happy path leaves users stranded with no way to recover and no path forward beyond abandoning the product. That gap separates software people tolerate from software people trust, and trust is what a good user experience is actually built on.
There is a certain irony in paying twice for the same product, once to build it fast and once to build it properly, but many companies are doing exactly that this year. When non-technical staff build a product through natural-language prompts with no governed environment underneath them, the resulting software doesn’t meet production conditions, and entropy in the codebase increments quickly.
As a consequence, a new software role was born: the vibe coding cleanup specialist. An experienced engineer hired specifically to stabilize or rebuild what got shipped without guardrails. As many companies are now realizing, the repair frequently costs more than building it properly the first time would have.
Unlike what you might think, this is not just a “startup thing”, and it doesn’t happen on brand new products alone. Unsupervised coding agents have affected codebases in established companies as well. Earlier this year, Amazon had at least two critical incidents on their e-commerce website related to AI-assisted code updates, which led to the creation of an internal rule demanding that senior engineers must sign off every AI-assisted change made by juniors or semi-seniors.
AI-assisted coding is standard practice in 2026. Whether that practice produces a solid product or a liability comes down to how much engineering judgment was built into the project before the agent ever started writing.
An experienced engineer adds judgment to a codebase, a sense of how things must be done. AI doesn’t apply those concepts by default. Instead, it needs to be guided according to the expectations and practices of each application context.
Judgment has a deeply technical half, and a harder one to name. The technical half is the ability to anticipate how a system will behave under load, under attack, and under six months of feature requests it was not initially designed for. The other half is closer to taste: the eye for a solution that is not merely correct but elegant, one that solves today’s problem while leaving the next engineer a foundation instead of a liability. Taste carries real weight in this context. It connects directly to cost, because the alternative to a good solution is a codebase that gets harder and more expensive to maintain and update every year it survives. That kind of judgment comes from years of watching what a shortcut costs months later, and from having paid that cost personally more than once.
An AI model optimizes for the answer that looks correct right now. It carries no memory of the impact a certain approach had on the last project, because it has no concept of a project persisting past this conversation. Left unsupervised, a codebase built only through AI agents tends toward high entropy: complexity and disorder both increase over time, showing up as duplicated logic, workarounds layered on top of problems already solved, and over-engineered solutions to simple tasks. I have watched this happen on real projects: without a continuous, deliberate effort to cut complexity back and preserve structure, a codebase drifts from a small number of coherent patterns toward a much larger number of inconsistent local solutions.
This decay isn’t new, and it’s not exclusive to AI. Codebases written entirely by hand have always drifted the same way. What changed is the rate: a human team writes slowly enough that the deterioration stays visible and correctable between releases, while a coding agent can generate thousands of lines in an hour, drastically accelerating that drift. Coding agents have a narrow objective: produce a correct answer to the task in front of them. Preserving the codebase’s architectural coherence sits outside that objective, which means someone has to design that preservation into the system on purpose.
Supervision isn’t optional. If you are shipping AI-assisted code, you or someone who genuinely understands software engineering has to be watching what gets written. But supervision alone stops holding the moment volume outpaces review capacity, and volume is exactly what vibe coding produces. What actually scales alongside it is judgment encoded into the environment itself, and encoding it well requires the experience and taste only a senior engineer can bring. An experienced engineer’s highest-leverage contribution to an AI-assisted codebase is designing a robust governance layer that keeps enforcing that judgment on everything an agent generates afterwards.
Building agentic systems raises the standard even further, because a system whose output is probabilistic rather than deterministic requires the discipline that ungoverned vibe coding tends to skip: selecting the model suited to the task it needs to solve, engineering prompts deliberately rather than iterating by feel, and instrumenting it with guardrails and other tooling running alongside the agent: static code analyzers, linters, duplicate code detectors, architecture and dependency analysis, automated code reviewers. What makes these tools valuable in an agentic setup is that they return precise, machine-readable results the agent can act on, feeding a feedback loop that corrects the work while it is being written instead of after it ships. Without that engineering work, what gets shipped is a prototype that behaves unpredictably in front of paying customers, a much worse outcome than a product that simply fails.
Vibe coding is a helpful tool for getting to a first working version of an idea, and using it that way is sound engineering practice. The mistake is shipping that first version without the governance that turns speed into something durable, and underrating software engineering experience.
Every developer uses AI to write code now, including the most senior engineers on any team. What sets them apart is judgment: knowing when a shortcut is safe to take, where it isn’t, and how to guide the model toward the right solution instead of the first one it offers. PwC’s 2026 Global AI Jobs Barometer shows where the returns concentrate: companies using AI to amplify experienced teams are growing productivity and headcount faster than companies using it to replace them.
AI amplifies engineering mastery, and unlocks potential in expert hands, which is why those experts should be building and advocating for the governance layer the whole team depends on. The point of that layer is that the knowledge stops being something one person applies by hand. Encoded into the environment, those practices become how the project works: the standard every contributor follows, and the same guide that steers human collaboration and AI agents alike.
The product your business depends on for revenue deserves the same rigor as any other capital investment. Getting to a demo is now a matter of hours. Getting to something your users can depend on under real-world conditions still requires governance infrastructure built by experienced engineers who have built and repaired production systems before, and who bring the judgment that AI tools cannot supply on its own. Acknowledging that difference early costs a fraction of doing it after your product is already carrying customers who noticed it break.
Paying twice is avoidable, and it costs a fraction to get this right the first time. Bridgenext helps enterprises put governed engineering practices in place while a product is still taking shape, so the cleanup bill never arrives. Let’s talk.
