Illusionen av snabbhet — När mer kod blir mindre värde
To a CEO, product owner, or CFO, AI-assisted development sounds like a dream: more code in less time should mean lower costs and faster launches. But in modern software engineering, the volume of code written has never been a metric for quality. Quite the opposite. AI models are trained to generate code based on what is statistically most likely, not what is most efficient for your specific business logic. The result is often bloated "boilerplate code"—features that technically work on the surface, but are overly complex and riddled with duplicated logic.
Mio Nilsson, CEO and developer at Plingot, sums up the risk well:
"AI makes mistakes, cuts corners, and gets sloppy. The end product might look fine on the surface—but when you look closer, there may be structural flaws that make future development significantly harder. In some cases, security vulnerabilities are built in, making it easier for outsiders to breach the system."
The symptoms usually appear after a few months: the initial features shipped at record speed, but as the system needs to scale or integrate with new APIs, everything grinds to a halt. A minor update suddenly requires patching three new bugs that popped up elsewhere. When analysis firm GitClear reviewed over 623 million code changes, they found a clear trend: the use of AI tools has led to a sharp increase in "code churn"—code so unsound that it must be rewritten shortly after release.
Three Warning Signs Your Codebase Has an AI Hangover
Sound familiar—development speed has picked up, but delivery reliability has dropped? Here are three classic signs your code has become unhealthy:
- Architecture has turned into Pick-up Sticks: Individual functions work great in their own little bubble, but no one on the team has a clear overview of how data actually flows through the system.
- Subtle security flaws: If code reviews happen too quickly, AI-generated shortcuts and security oversights slip straight into production.
- The team loses its grip on the business logic: When developers act primarily as "editors" approving AI proposals instead of thinking through the logic from scratch, deep understanding of your business model fades away.
Kod är billigt, hållbar systemarkitektur är det som kostar
Writing code syntax has effectively become a commodity—something a machine can churn out in seconds. What has become far more valuable, and significantly harder, is what happens before code is written: data modeling, security architecture, and the ability to translate complex business processes into a maintainable system.
This is where senior expertise matters most. An AI can assemble a room in seconds, but it cannot draft the blueprint for a skyscraper designed to withstand thirty years of storms. Or as Mio Nilsson formulates the golden rule:
"You're in deep water if you ask AI to build things you wouldn't be capable of building yourself."
Så botar ni baksmällan — 4 steg till en sund kodbas
Turning back the clock and ditching AI is neither realistic nor smart. The key is implementing the right controls so AI tools support craftsmanship rather than replacing it.
- Ställ högre krav på kodgranskning (Pull Requests): Inför regeln att ingen får släppa igenom kod som hen inte själv kan förklara i detalj och ta fullt ansvar för.
- Sätt tydliga ramverk för arkitekturen: Låt AI hjälpa till med isolerade funktioner, men låt aldrig en språkmodell definiera hur databaser, API-gränssnitt eller säkerhetslager ska byggas.
- Prioritera kontinuerlig refaktorisering: Avsätt tid i varje sprint för att städa upp, förenkla och ta bort kod. Den bästa koden är ofta den kod som aldrig behövde skrivas.
- Gör en oberoende teknisk granskning: Om ni känner att systemet börjar bli svårroddat – eller om ni står inför en investering – låt utomstående experter göra en djupdykning i källkoden.
Building software ultimately isn't about who can write the most lines of code in the shortest time, but who creates the most value over time without getting bogged down in technical debt. No matter how sharp AI tools become, it's the craftsmanship behind the code that dictates whether your system stands the test of time.
Feeling like your codebase has become hard to navigate, or looking to conduct a thorough audit before your next big leap? At Plingot, we help companies build scalable systems from the ground up—and evaluate and clean up existing software through our Tech Due Diligence.
Get in touch with us at Plingotand let's sit down over a coffee to review your architecture.
