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I鈥檓 a firm believer that 鈥� like oil and water 鈥� vibes and coding don鈥檛 mix particularly well.

When we code, we鈥檙e following rules and concepts to make sure programs are built on proper foundations. In contrast, vibes are about intuition and what feels right. Mash them together and you will inevitably end up with inconsistent software with inherent reliability issues.

In the short-term, vibe coding is an approach that creates confusion and buggy problems. But in the long-term, the stakes are far higher. This potent mix is a recipe that could lead to model collapse.

The long-term risks of sloppy code

Vibe coding replaces experience with vision. And while it can be a great way of experimenting and opening up the magic of software development to more people, when used to create code that will actually power systems and be relied on by others, it鈥檚 not fit for purpose.

Products built on AI-generated code that hasn鈥檛 been properly stress-tested weaken the integrity of everything built on top of them. Every time we lean on AI-generated code, or train new models on the outputs of vibe-coded ones, we are polluting the data sets and weakening the infrastructure that forms the foundation of software.

It鈥檚 a feedback loop where slop feeds off slop. The code gets fuzzier, the bugs more frequent, the quality of results steadily worse. Ultimately, the very models themselves will be unable to tell fact from fiction 鈥� rendering results and outputs useless.

This is . Constant hallucinations and errors, , and a breakdown of trust among customers.

When we rely too heavily on approaches that cut corners 鈥� skipping testing, overlooking governance and avoiding hard questions 鈥� we won鈥檛 just get bad apps, we鈥檒l break the foundations that models rely on.

How to build better

It鈥檚 time to re-establish some fundamentals. To avoid model collapse, here鈥檚 what we can do:

Double-down on data governance. If you don鈥檛 know where your data came from, or whether you can trust it, you鈥檙e essentially building on sand.

Build the infrastructure needed to accurately classify and label documents, before enriching them with top-quality metadata. Then ensure this data is stored correctly, backed up, and given the right security and permissions to enable good governance.

If you鈥檙e keen to optimize delivery, there are AI data governance tools that can do it for you. Establishing solid governance gives you greater control over the AI systems that work on your data. (Oh, and just another reminder to .)

Train engineering muscle

While AI can speed up development and enable teams to ship code faster, leaning too hard on it can cause core skills to wane. It鈥檚 vital to train developers properly and continue to invest in junior talent. All colleagues need to understand what 鈥済ood鈥� code looks like, so they can ensure the quality remains high and become the senior leaders of tomorrow.

Building coding skills without shortcuts should be a focus for junior developers to ensure that the next generation of tech talent understands what they are building at a foundational level, before they reach for optimization tools.

Aim for real-world feedback

It鈥檚 not enough to test your products in a sandbox and call it a day. An important part of the development cycle is testing software in the real context it鈥檒l exist in. For example, beta testing your products on users might take a lot of time, but it will stress-test systems in ways that developers might never have anticipated.

In other words: don鈥檛 rush feedback. Spending time observing and measuring how your products perform in the real world means you can catch and resolve quality issues early and build reliability into a final product.

Discipline means stability

The next wave of startups have a choice: build with discipline or build on slop. One scales trust. The other scales technical debt. By resisting the temptation to cut corners, we can ensure strong foundations and avoid model collapse.

is chief technology officer at AI company , whose tech and services help teams find, make sense of and retain control over their data. He leads Aiimi’s research and development on new and emerging technologies, with a particular focus on AI. When Maker is not at his computer, you will find him either at the gym or walking his dog.

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