Thank you everyone for your input. I have created a separate community, [email protected], for AI coding related discussions.

I agree with you on that point, and the same could be said about the meat and dairy industry. However I don’t think the answer is censoring discussions about cooking beef or chicken.

You can’t compare racist posts, which are a form of hate speech and a breach of this instance’s code of conduct, with discussions about topics that you don’t agree with.

Good question. I have asked this very question https://programming.dev/post/45013854

Expensive as hell! 🤑

Yegge describes Gas Town as “expensive as hell… you won’t like Gas Town if you ever have to think, even for a moment, about where money comes from.” He’s on his second Claude account to get around Anthropic’s spending limits.

I can’t find any mention online of the per-account limits, but let’s conservatively assume he’s spending at least $2,000 USD per month, and liberally $5,000.

I am not surprised that there are parallels between vibe coding and gambling:

With vibe coding, people often report not realizing until hours, weeks, or even months later whether the code produced is any good. They find new bugs or they can’t make simple modifications; the program crashes in unexpected ways. Moreover, the signs of how hard the AI coding agent is working and the quantities of code produced often seem like short-term indicators of productivity. These can trigger the same feelings as the celebratory noises from the multiline slot machine.

Where did you get the impression that the author is an inexperienced developer and finance bro? The introduces himself as someone who started programming from the age of eleven.

I’m Michael Arnaldi, Founder and CEO of Effectful Technologies — the company behind Effect, the TypeScript library for building production-grade systems. I’ve been programming most of my life. I started at 11 with the goal of cracking video games. Since then, I’ve written code at every level: from kernel development to the highest abstractions in TypeScript.

I think the most interesting, and also concerning, point is the eighth point, that people may become busier than ever.

After guiding way too many hobby projects through Claude Code over the past two months, I’m starting to think that most people won’t become unemployed due to AI—they will become busier than ever. Power tools allow more work to be done in less time, and the economy will demand more productivity to match.

Consider the advent of the steam shovel, which allowed humans to dig holes faster than a team using hand shovels. It made existing projects faster and new projects possible. But think about the human operator of the steam shovel. Suddenly, we had a tireless tool that could work 24 hours a day if fueled up and maintained properly, while the human piloting it would need to eat, sleep, and rest.

In fact, we may end up needing new protections for human knowledge workers using these tireless information engines to implement their ideas, much as unions rose as a response to industrial production lines over 100 years ago. Humans need rest, even when machines don’t.

This does sound very much like what Cory Doctorow refers to as a reverse-centaur, where the developer’s responsibility becomes overseeing the AI tool.

This article is quite interesting! There are a few standout quotes for me:

On one hand, we are witnessing the true democratisation of software creation. The barrier to entry has effectively collapsed. For the first time, non-developers aren’t just consumers of software - they are the architects of their own tools.

The democratisation effect is something I’ve been thinking about myself, as hiring developers or learning to code doesn’t come cheap. However, if it allows non-profits to build ideas that can make our world a better place, then that is a good thing.

We’re entering a new era of software development where the goal isn’t always longevity. For years, the industry has been obsessed with building “platforms” and “ecosystems,” but the tide is shifting toward something more ephemeral. We’re moving from SaaS to scratchpads.

A lot of this new software isn’t meant to live forever. In fact, it’s the opposite. People are increasingly building tools to solve a single, specific problem exactly once—and then discarding them. It is software as a disposable utility, designed for the immediate “now” rather than the distant “later.”

I’ve not thought about it in this way but this is a really good point. When you make code cheap, it makes it easier to create bespoke short-lived solutions.

The real cost of software isn’t the initial write; it’s the maintenance, the edge cases, the mounting UX debt, and the complexities of data ownership. These “fast” solutions are brittle.

Though, as much as these tools might democratise software development, they still require engineering expertise to be sustainable.

Thank you! I’ve added the image to the post as well.

I use AI coding tools, and I often find them quite useful, but I completely agree with this statement:

And if you think of LLMs as an extra teammate, there’s no fun in managing them either. Nurturing the personal growth of an LLM is an obvious waste of time.^___

At first I found AI coding tools like a junior developer, in that it will keep trying to solve the problem, and never give up or grow frustrated. However, I can’t teach an LLM, yes I can give it guard rails and detailed prompts, but it can’t learn in the same way a teammate can. It will always require supervision and review of its output. Whereas, I can teach a teammate new or different ways to do things, and over time their skills and knowledge will grow, as will my trust in them.

My understanding of how this relates to Jevons paradox, is because it had been believed that advances in tooling would mean that companies could lower their headcount, because developers would become more efficient, however it has the opposite effect:

Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we’d need fewer developers. Each one instead enabled us to build more software.

The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don’t reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.

Even when I share these articles in the AI community, they get voted down. 🫤 I know these articles aren’t popular, because there is quite a lot of prejudice against AI coding tools. However, I do find them interesting, which is why I share them.

This quote from the article very much sums up my own experience of Claude:

In my recent experience at least, these improvements mean you can generate good quality code, with the right guardrails in place. However without them (or when it ignores them, which is another matter) the output still trends towards the same issues: long functions, heavy nesting of conditional logic, unnecessary comments, repeated logic – code that is far more complex than it needs to be.

AI coding tools definitely helpful with boilerplate code but they still require a lot of supervision. I am interested to see if these tools can be used to tackle tech debt, as often the argument for not addressing tech debt is a lack of time, or if they would just contribute it to it, even with thorough instructions and guardrails.

This quote really sums up the situation:

This is a technical and business challenge, but also an ethical crisis. Anyone who cares to look can see the tragic consequences for those who most need the help technology can offer. Meanwhile, the lies, half-truths, and excuses made by frontend’s influencer class are in defence of these approaches are, if anything, getting worse.

Through no action of their own, frontend developers have been blessed with more compute and bandwidth every year. Instead of converting that bounty into delightful experiences and positive business results, the dominant culture of frontend has leant into self-aggrandising narratives that venerate failure as success. The result is a web that increasingly punishes the poor for their bad luck while paying developers huge salaries to deliver business-undermining results.

The developer community really needs to be building websites that work on all devices and connections, and not just for those who can afford the latest technology and high-speed internet connections.

The way the author described programming in 2025 did make me chuckle, and I do think he makes some excellent points in the process.

It’s 2025. We write JavaScript with types now. It runs not just in a browser, but on Linux. It has a dependency manager, and in true JavaScript style, there’s a central repository which anyone can push anything to. Nowadays it’s mostly used to inject Bitcoin miners or ransomware onto unsuspecting servers, but you might find a useful utility to pad a string if you need it.

In order to test our application, we build it regularly. On a modern computer, with approximately 16 cores, each running at 3 GHz, TypeScript only takes a few seconds to compile and run.

As the author notes, it is very impressive what generative AI can produce these days.

The frontier of what the LLMs can do has moved since the last time I tried to vibe-code something. I didn’t expect to have a working interpreter the same day I dreamt of a new programming language. It now seems possible.

However, as they point out, there’s definitely downsides to this approach.

The downside of vibe coding the whole interpreter is that I have zero knowledge of the code. I only interacted with the agent by telling it to implement a thing and write tests for it, and I only really reviewed the tests. I reckon this would be an issue in the future when I want to manually make some change in the actual code, because I have no familiarity with it.

What about developers who are required to use AI as part of their job?

I know what you mean. Quite often when I’ve worked in a project where there is a pull request template, a lot of the time people don’t bother to fill it out. However, in an ideal world, people would be proud of the work that they’ve delivered, and take the time to describe the changes when raising a pull request.

I’m not an architect, but I do dislike how much of development work has AWS wrangling, dealing with the architectural hoops that are mentioned in the article