Max Headroom 
UNCLASSIFIED
This article originally appeared on Medium. Tim O’Brien has given us permission to repost here on Radar.
One of the most unexpected changes in software development right now comes from code generation. We’ve all known that it could speed up certain kinds of work, but what’s becoming clear is that it also reshapes the economics of libraries, frameworks, and even the way we think about open source.
Just to be clear, I don’t view this as a threat to the employment of developers. I think we’ll end up needing more developers, and I also think that more people will start to consider themselves developers. But I do think that there are practices that are expiring:
- Purchasing software—It will become more challenging to sell software unless it provides a compelling and difficult-to-reproduce product.
- Adopting open source frameworks—Don’t get me wrong, open source will continue to play a role, but there’s going to be more of it, and there will be fewer “star stage” projects.
- Software architects—Again, I’m not saying that we won’t have software architects, but the human process of considering architecture alternatives and having very expensive discussions about abstractions is already starting to disappear.
Why Are You Paying for That?
Take paid libraries as an example. For years, developers paid for specific categories of software simply because they solved problems that felt tedious or complex to recreate. A table renderer with pagination, custom cell rendering, and filtering might have justified a license fee because of the time it saved. What developer wants to stop and rewrite the pagination logic for that React table library?
Lately, I’ve started answering, “me.” Instead of upgrading the license and paying some ridiculous per-developer fee, why not just ask Claude Sonnet to “render this component with an HTML table that also supports on-demand pagination”? At first, it feels like a mistake, but then you realize it’s cheaper and faster to ask a generative model to write a tailored implementation for that table—and it’s simpler.
Most developers who buy software libraries end up using one or two features, while most of the library’s surface area goes untouched. Flipping the switch and moving to a simpler custom approach makes your build cleaner. (I know some of you pay for a very popular React component library with a widespread table implementation that recently raised prices. I also know some of you started asking, “Do I really need this?”)
If you can point your IDE at it and say, “Hey, can you implement this in HTML with some simple JavaScript?” and it generates flawless code in five minutes—why wouldn’t you? The next question becomes: Will library creators start adding new legal clauses to lock you in? (My prediction: That’s next.)
The moat around specific, specialized libraries keeps shrinking. If you can answer “Can I just replace that?” in five minutes, then replace it.
Did You Need That Library?
This same shift also touches open source. Many of the libraries we use came out of long-term community efforts to solve straightforward problems. Logging illustrates this well: Packages like Log4j or Winston exist because developers needed consistent logging across projects. However, most teams utilize only a fraction of that functionality. These days, generating a lightweight logging library with exactly the levels and formatting you need often proves easier.
Although adopting a shared library still offers interoperability benefits, the balance tilts toward custom solutions. I just needed to format logs in a standard way. Instead of adding a dependency, we wrote a 200-line internal library. Done.
Five years ago, that might have sounded wild. Why rewrite Winston? But once you see the level of complexity these libraries carry, and you realize Claude Opus can generate that same logging library to your exact specifications in five minutes, the whole discussion shifts. Again, I’m not saying you should drop everything and craft your own logging library. But look at the 100 dependencies you have in your software—some of them add complexity you’ll never use.
Say Goodbye to “Let’s Think About”
Another subtle change shows up in how we solve problems. In the past, a new requirement meant pausing to consider the architecture, interfaces, or patterns before implementing anything. Increasingly, I delegate that “thinking” step to a model. It runs in parallel, proposing solutions while I evaluate and refine. The time between idea and execution keeps shrinking. Instead of carefully choosing among frameworks or libraries, I can ask for a bespoke implementation and iterate from there.
Compare that to five years ago. Back then, you assembled your most senior engineers and architects to brainstorm an approach. That still happens, but more often today, you end up discussing the output of five or six independent models that have already generated solutions. You discuss outcomes of models, not ideas for abstractions.
The bigger implication: Entire categories of software may lose relevance. I’ve spent years working on open source libraries like Jakarta Commons—collections of utilities that solved countless minor problems. Those projects may no longer matter when developers can write simple functionality on demand. Even build tools face this shift. Maven, for example, once justified an ecosystem of training and documentation. But in the future, documenting your build system in a way that a generative model can understand might prove more useful than teaching people how to use Maven.
The Common Thread
The pattern across all of this is simple: Software generation makes it harder to justify paying for prepackaged solutions. Both proprietary and open source libraries lose value when it’s faster to generate something custom. Direct automation displaces tooling and frameworks. Frameworks existed to capture standard code that generative models can now produce on demand.
As a result, the future may hold more custom-built code and fewer compromises to fit preexisting systems. In short, code generation doesn’t just speed up development—it fundamentally changes what’s worth building, buying, and maintaining.
AI & ML, Commentary
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