Things are changing fast in the world of software. Big language models, or LLMs, are getting better and better at coding. This means huge shifts could be on the way for how software is built and who builds it.

Vibe Coding Gets Better Fast

Recently, a person tried using vibe coding again after six months. The results blew them away. The AI agent they used before made lots of mistakes. But this time? It handled the project almost perfectly on the first try. That’s a huge leap in just half a year. The person said they’re still amazed and don’t see that excitement fading soon.

One of the coolest parts of vibe coding is fixing bugs. Normally, error messages can be hard to decipher. They take humans a lot of time to figure out. But AI can now read those messages and fix the problems in seconds. For example, dependency issues—those deep, tangled chains of software parts—are a nightmare for humans. But AI can untangle and repair them quickly. This means developers will spend less time troubleshooting and more time creating.

This progress could lead to a boom in what some call “boutique software.” These are small, specialized apps or websites that never got built because making them was too expensive. Now, with AI reducing costs, even tiny projects could become a reality. Think of it like TikTok for software—short, simple, and fast. Small ideas that once seemed unprofitable might now turn into full-fledged apps, opening up new creative possibilities.

Why Large Language Models Are So Good at Coding

LLMs are text-based AI systems. They take in writing and spit out more writing. Since code is just text, these models are naturally good at creating it. They learn from tons of existing code, which makes them excellent at writing new code snippets or fixing bugs. Because of this, AI can speed up many parts of software development.

But this creates a tricky question. Up to now, humans have written nearly all the code that trains these models. As AI gets better, humans are writing less code. So, what will LLMs learn from next? Some think they’ll learn from their own output, improving on their own over time. Others believe humans will keep designing the building blocks—like libraries and frameworks—and AI will just build on those.

In the future, maybe AI will do almost everything. Humans might just describe what they want, and AI will generate the code automatically. It’s tempting to think we’ll always need software developers, but history shows otherwise. Fewer farmers and factory workers are needed today than decades ago. The same could happen with developers. The question is: how many will be needed in the future?

The Changing Role of Developers and What’s Next

The story of the “hidden figures” in the space program highlights how much humans used to do manually. Black women, called “computers,” did complex calculations by hand or with early computers. Today, those same calculations are done with simple tools like spreadsheets. AI might replace some tasks that once required human skill, making the role of developers change dramatically.

It’s hard to predict exactly where all this is headed. AI acts like a powerful catalyst, pushing software development into uncharted territory. No one really knows what the landscape will look like next week, let alone next year. But one thing’s certain: it’s going to be a wild ride, full of surprises and innovations.

As AI continues to evolve, it’s clear that the future of software development will look very different from today. Some jobs may disappear, but new opportunities will also emerge. Developers will need to adapt and learn new skills, perhaps focusing more on designing systems and guiding AI rather than writing every line of code themselves.

In the end, AI’s rise is not about replacing humans but transforming their role. The key will be to leverage these new tools to create faster, smarter, and more innovative software. Whatever happens next, one thing’s for sure—things are going to be pretty amazing.

Inspired by

• https://www.infoworld.com/article/4083424/some-thoughts-on-ai-and-coding.html

Sources

• gmpg.org
• imdb.com
• wikipedia.org
• nickhodges.com
• podigee.com