AI Driven Software Engineering
We're missing the promise, and the risks, for the hype
At the time of writing the marketplace of AI assisted development tooling is less than three and a half years old. Chat based LLM tooling and now agentic assistants are just exploding into the marketplace, there are 244 tools on the AI Native Dev’s landscape, with 34 in the editor category alone.
It is extremely clear that we are in the midst of a disruption wave crashing around us, we know from history that in the midst of that prediction is a fools game, and yet that isn’t stopping the hype machine from creating noise mostly about the extremes.
While the AI wave is large, fast and incredibly uncertain, our way through is learning by experimentation, as it always has been
At one extreme is the ‘within 2 years we won’t even need engineers’, which has already been part of the discourse for a couple of years. Yes folks, next year will be the year of the linux desktop. Of course this is being used to drive layoffs and fears in some businesses, that is both bad and predictable. While those cuts hurt they are a long way off of the amount that would indicate a true replacement level belief among company executives.
The other extreme is the skeptic pole, something like ‘all this vibe coded crap is gonna burn everything down’. There is an absolute ton, several books worth really, to say about making things work as products and companies scale, but vibe coding really just looks to empower folks at very early idea experimentation. Any reasonable developer experience and product focused engineer I think should celebrate this, and then offer to roll up the sleeves to make the things work for real.
When taken in the large, across the entire industry which produces software (which is everywhere, so thats kind of everyone), we will experience the landing of this wave in multitudes. The extremes will be true to some extent, but the majority experiences I think will drop again into two clusters, and that really comes down to one choice.
do you want to amplify bad habits, or rebuild better habits and processes and amplify those
Fundamentally the idea is that AI based tooling lowers your effort (toil, cognitive load, etc) required for similar lift, this implies it’s a force multiplier on your habits; your choice is do you want to amplify bad habits, or rebuild better habits and processes and amplify those. Funnily enough DevOps at it’s core has always been about this question, and yet gave birth to a bunch of products and dogma that promised to be one stop utopia, returning to the core mental processes of this will separate success from failure.
This choice does apply, as always, to individual engineers and to engineering orgs in the large, the micro and the macro view. The early numbers are not super encouraging, DORA’s most recent state of DevOps report and a special impact of AI report suggest that we’ve taken these tools and were just slinging more code, which given our flow models has had the predictable result of harming effectiveness and outcomes.
To leaders I say get serious about refactoring demands, incentives and the support you offer folks to rebuild their workflows, we are a long way from maturity and stability in the marketplace here, but waiting it out is a bad strategy; you should have been doing this anyway. Because waiting is bad, also get comfortable with a ton of experimentation with fuzzy ROI projections, looking for high probability long term outcome choices is hard enough to be impossible in the current climate.
For my fellow engineers, for years we’ve complained about technical debt, so much so that the term has really lost meaning. The core of that has always been roughly, yes, we know better and that we need to write tests and documentation (irony being AI tooling works best when these do exist), but we just don’t have the time. Well, now those things are easier to do, much easier really, so it’s definitely the time to start doing it better.
Part of the reason for the negative reaction to vibe coding is because that is pretty close in truth to the at large truth about software engineering. We’re already drowning in code that is hard to wrestle with, the idea of massively increasing that problem is rightly terrifying.
While the AI wave (or hurricane maybe) is large, fast and incredibly uncertain, our way through is learning by experimentation, as it always has been.