created 2025-03-06, & modified, =this.modified
tags:y2025programmingcsai
Title
Author says programming is not over but changing, in a similar in some respects but paradigmatically different way that it has been changing since inception. What a skillful programmer will do, will be to utilize this new technology to focus on “higher level” creativity that adds value. This will distinguish them from the fact that the majority of people with access to an LLM (“ordinary people”) can do things normally in the domain of a specialist.
There’s a lot of chatter in the media that software developers will soon lose their jobs to AI. I don’t buy it.
First programmers connected physical circuits. There are higher and higher levels of abstraction with developments in technology (assembly to higher level languages like FORTRAN) and thus “what it means to program” changes at each step.
BASIC (Beginners’ All-purpose Symbolic Instruction Code) was the first interpreted language to hit big, and though initially seen “as a toy” it excelled due to being easier to debug, and subsequently became the norm.
Programmers no longer were required to familiarize themselves with lower level implementation details (largely handled by the operating system) and could concern themselves with addressing problems.
Further paradigm shifts
- web programming
- separation between frontend and backend
- mobile device relevancy
The complexity of this was often hidden behind frameworks, APIs and libraries.
In each of these waves, old skills became obsolescent—still useful but no longer essential—and new ones became the key to success. There are still a few programmers who write compilers, thousands who write popular JavaScript frameworks and Python libraries, but tens of millions who write web and mobile applications and the backend software that enables them. Billions of users consume what they produce.
Might this time be different?
It now seems a “nonprogrammer” can simply talk to an LLM or specialized software agent in “plain English” to get back a useful prototype. CHOP, or Chat-oriented-programming.
The author does not buy this.
When there’s a breakthrough that puts advanced computing power into the hands of a far larger group of people, yes, ordinary people can do things that were once the domain of highly trained specialists. But that same breakthrough also enables new kinds of services and demand for those services. It creates new sources of deep magic that only a few understand.
Smart developers who adopt the technology will be in demand because they can do so much more, focusing on the higher-level creativity that adds value.
Juniors will not be replaced, but those who “cling to the past” rather than embracing new paradigms and tools will be.
Economic Historian James Bressen studied how the first Industrial Revolution played out in textile mills. As skilled crafters were replaced by machines unskilled labor, human wages were also depressed. But it took just about as long for an apprentice craftsman to reach the full wages of a skilled journeyman as it did for entry-level to reach full pay and productivity. The workers in both regimes were actually both skilled workers, but they had different skills.
Workers needed new skills not only to use the machines but to repair them, to improve them, to invent the future that they implied but had not yet made fully possible. All of this happens through a process that Bessen calls “learning by doing.”
Jevons paradox
In economics, the Jevons paradox occurs when technological advancements make a resource more efficient to use (thereby reducing the amount needed for a single application); however, as the cost of using the resource drops, if the price is highly elastic, this results in overall demand increases causing total resource consumption to rise.
Often increases in demand lead to increases in employement.
Models don’t have memory the way humans do. Even with large context windows they struggle with metacognition. As result humans are able to co-operate with AI to provide context.
Schillace: Large language models (LLMs) and other AI systems are attempting to automate thought
Addy Osmani: While engineers report being dramatically more productive with AI, the actual software we use daily doesn’t seem like it’s getting noticeably better.
When you watch a senior engineer work with AI tools like Cursor or Copilot, it looks like magic. They can scaffold entire features in minutes, complete with tests and documentation. But watch carefully, and you’ll notice something crucial: They’re not just accepting what the AI suggests…. They’re applying years of hard-won engineering wisdom to shape and constrain the AI’s output. The AI is accelerating their implementation, but their expertise is what keeps the code maintainable. Junior engineers often miss these crucial steps. They accept the AI’s output more readily, leading to what I call “house of cards code” – it looks complete but collapses under real-world pressure.