The insurance analogy doesn't work - programming skills exist on a spectrum of daily relevance. Insurance becomes relevant in rare emergencies. Programming skills are becoming less relevant in day-to-day.

What I pointed out in my post is a trend I notice where an LLM can do more and more of a developer's work. Nowhere did I claim LLMs can replace human developers today, but when a technology consistently reduces the need for manual programming while improving its capabilities, the trajectory is clear. You can disagree with the timeline, but the transformation is already underway.

I posted on HN precisely because I wanted rigorous technical discussion, not validation.

LLMs have already improved sufficiently that people are worried their programming skills are decaying, debugging skills included (based on the article I referenced). I'm curious to see how you envision LLMs improving without this not becoming even more pronounced. Isn't that the definition of a skill slowly becoming irrelevant? The fact that you can see you are not using it as much?

As for the reception, I did not expect it to be positive. People usually have a strong negative emotional reaction when you suggest their skills are, or are going to become, less relevant.

I agree that you still need programming skills (today, at least). Yet people are using those programming skills less and less, as you can clearly see in the article [1] I referenced.

You are also making the assumption that LLMs won't improve, which I think is shortsighted.

I fully agree with the part about the job becoming more like product management. I would like to cite an excerpt of a post [2] by Andrew Ng, which I found valuable:

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. (...) Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce.

To address your last point - no, I am not saying people should skip learning a whole way of thinking. In fact, the skills I outline for the future (supervising AI, evaluating results) all require understanding programming concepts and system thinking. They do not, however, require manual debugging, writing lines of code by hand, a deep understanding of syntax, reading stack traces and googling for answers.

[1] https://nmn.gl/blog/ai-illiterate-programmers

[2] https://www.deeplearning.ai/the-batch/issue-284/

My post did not take the position that understanding problems isn't important.

Using LLMs can even help you understand the problem better. And it can bring you towards the solution faster. Using an LLM to solve a problem does not prevent understanding it. Does using a calculator prevent us from understanding mathematical concepts?

Technical understanding will still be valuable. Typing out code by hand will not.

The trajectory of LLMs "routinely producing incorrect results" is heading downwards as we are getting more advanced reasoning models with test-time compute.

I don't know whether you used some of the more recent models like Claude 3.5 Sonnet and o1. But to me it is very clear where the trajectory is headed. o3 is just around the corner, and o4 is currently in training.

People found value even in a model like GPT 3.5 Turbo, and that thing was really bad. But hey, at least it could write some short scripts and boilerplate code.

You are also comparing mathematical computation - which has only 1 correct solution - with programming, where the solution space is much broader. There are multiple valid solutions. Some are more optimal than others. It is up to the human to evaluate that solution, as I've said in the post. Today, you may even need to fix the LLM's output. But in my experience, I'm finding I need to do this far less often than before.

You make a good point and those kind of senior engineer skills may be the least affected. My post does not argue against that. It argues that writing code manually may quickly become obsolete.

What I am trying to say is that people who see the output of their work as "code" will be replaced just like human computers did. I believe even debugging will be increasingly aided by AI. I do not believe that AI will eliminate the need for system understanding, just to be clear.

Then again, you might argue that writing lines of code and manually debugging issues is exactly what builds your understanding of the system. I agree with that too, I suppose the challenge will be maintaining deep system knowledge as more tasks become automated.