In February I decided to move to the command line. I started assembling the tools to live primarily in command-line mode. One of the tools I missed badly was a good Markdown reader. I searched far and wide. There were not many good options. Glow was the most convincing choice, but I was hitting bugs and limitations. So I decided to build one for myself. Armed with Claude Code, I built one in a couple of weeks - a command-line Markdown viewer with a simple editor and readability metrics. I shared it with some friends and decided to let it cool down before sharing it widely.

Then, a couple of months later, I started observing something interesting. There was a wave of Markdown viewers and editors. I saw new Markdown tools when I ran brew update. I saw announcements in my Hacker News and Reddit feeds. Of course, it was not just command-line tools; a lot of them were GUI apps. Markdown tools are a low-barrier entry point for tinkering with AI code generation. So, have I given up my Markdown editor? No, not yet. I have tested a few, some carefully crafted, some completely vibe-coded. Not many of them have the ergonomics and the speed I want. So I am still sticking with my creation.

AI is leading us to an (or another?) era of mass-produced software, built in bedrooms, dorms and coffee shops. In my opinion, we already have an overproduction problem with consumer goods: cheap parts, fragile assembly, generic interfaces, and keysmash branding. If we are not careful, we will have digital products with cheap parts, fragile integrations and generic interfaces flooding our app stores.

The long tail is a known phenomenon in the digital world - whether in digital products or digital distribution. AI is lengthening the long tail, at the end of which are single-user systems and single-use systems. It is okay to build DIY tools that stay within one’s environment. In a way, spreadsheets were a platform for DIY tools. What makes it difficult is the noise it generates in the public market. It is acceptable for AI-generated tools to compete in the market and replace existing ones that are not up to the mark, and a fair market does not and should not limit anyone’s entry. However, at what cost? The distribution ecosystems, such as app stores and GitHub, and even human attention are getting strained by the number of new apps and tools. And over time, we will have a lot of abandonware, creating an invisible-but-real software wasteland.

We will learn to improve the quality and reliability, using libraries, agentic frameworks and newer design patterns that we are yet to define. That might fix the abandonware problem, and we may very well move into another cycle of build-vs-buy in which the build might dominate. However, the core issue of overproduction may still remain.

In the 19th century, the world faced a similar situation during the Industrial Revolution - for example, when mechanised cotton spinning made textiles quite cheap. Foreign markets were an option at that time. Today AI is a global phenomenon. Then followed a series of crises and price deflations, cartels and consolidation. Eventually the world brought supply and demand back into balance, through consolidation of competitors and supply generating its own demand.

Will AI make software cheaper? If so, will enterprises consume more software by building custom solutions than by buying off-the-shelf software? Is overproduction of goods and tools necessary? Is there an anarchy of production in software? Are the current costs of AI artificially low and are they incentivising overproduction? We have a lot to question, think about and answer in the coming days.