All the evidence I see continues to suggest that good engineering discipline is not just desirable, but essential when using GenAI for coding. But that’s exactly what the vast majority of software engineers – and teams – lack.

Take Test-Driven Development (TDD) for example. I keep hearing that one of the most effective ways to stay in control of GenAI output is to take a test-first approach (“Test-Driven Generation” or TDG as its becoming known) – and I agree based on experience. On one hand, I’m excited by the idea of a TDD renaissance. However, I saw something recently suggesting only around 1% of code is written that way. Anecdotally, most developers I speak to who say they know TDD, don’t actually understand what it is. It’s a clear example of the skills gap we’re dealing with.

Let alone TDD, again, everything I see and hear on the ground suggests effective assisted development also relies on having comprehensive automated tests and the ability to release frequently in small batches. Many teams have neither. Some have a few tests. Most can only release a few times a month because the rely on long, manual regression cycles due to their lack of automated test coverage.

I’m not convinced by arguments that GenAI will improve code quality (vs experienced engineers not using GenAI). The skills gap is part of the problem – but also, studies like GitClear’s earlier this year already show a significant drop in code quality linked to GenAI use.

At best, good practices will act as damage limitation.

GenAI coding could be a turning point. But most teams simply aren’t equipped to handle it. And unless that changes – quickly – which seems unlikely given how long these practices have existed without widespread adoption, we’re likely heading for a wave of poor-quality code, delivered at speed.