Recently, a colleague of mine was working on a mathematical model in Excel. He asked Copilot to solve a complex problem. The answer the spreadsheet produced wasn’t quite right. He asked Copilot what it did to figure out the answer. It started spitting out Python code.
My colleague was not a developer. He had never written a line of code in their life. He had no interest in becoming a developer. However, at that moment, he had become one.
When it comes to software development, I’ve referred to AI as an “abstraction layer”. My data scientist friends hate when I say that, but the reality is that we’ve been following a natural trend in programming abstraction since the dawn of Babbage’s Difference engine:
At each level, the abstraction layer got further and further between the programmer and the underlying binary. At the same time, the gamut of who could become a “programmer” got larger. We’re now at a point where the abstraction layer is so great that programmers no longer consistently identify themselves as programmers.
Put another way, we no longer have “professional” and “citizen” developer – we just have developers. And some of them are accidental developers – they don’t even know they’re writing code.
My colleague’s interaction with AI is not a unique one. I’ve seen this with others as well, and we’ve posited the “tools creating tools” space for some time when it comes to AI.
However, we’ve glossed over a significant challenge: In the more than a century we’ve been perfecting software engineering, programmers have focused on writing secure, reliable, and redundant code at scale. All of the steps of the traditional software development lifecycle (SDLC): analyze/plan, design, build/maintain, test, and deliver were formalized under the assumption that humans manage each stage.
In my conversations with accidental developers, however, they’ve handed off the wheel of these stages to AI – if they know there are stages at all. When I ask them if they’ve reviewed the code generated, they rarely do. If they do, they often don’t understand it. Some have the wherewithal to ask AI to test the code generated, but that’s an explicit ask and often done with the same AI that wrote the code (a situation that would be frowned upon with human developers). Delivery has its own challenge: “it ran on my laptop” takes on entirely new meaning when AI has installed packages and a container runtime on your machine you didn’t install yourself and now need to replicate in the cloud. And proper analyzing/planning and designing beforehand? Forget about it.
This hasn’t been helped by the fact that we’ve seen a shift in coding agents grow in capabilities to do multiple parts of the SDLC. We’ve gone from multiple agents from different vendors communicating intent across multiple phases of the SDLC, to single agents doing everything. Separation of duties, this is not.
It would be silly to presume we can close Pandora’s Box at this point. Now that the agentic software development genie is out of the bottle, we can’t (and shouldn’t) tell people, “Don’t write code”. Two reasons: 1) Fundamentally, programming should be open to all and 2) As made clear in this blog, some people don’t even know they’re writing code to begin with. This is especially true as tools creating tools cascades to multiple levels of hammers making other hammers.
In short, we need to solve this tactically and strategically:
Spec-driven development practices will help this, but there are fundamental requirements that must be built into the models themselves. The onus is on users and model creators to work together and build software securely with AI, whether they call themselves developers or not.
Forrester has a full team of analysts covering the revolution of agentic software development and the dawn of the accidental developer – I am the team’s Reesearch Director. Schedule a guidance session with us if you’re a client to discuss the ramifications of this, or leverage our Forrester AI for instant insight.
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