AI-Driven Software Fluidity

What Is the Thesis of Software Fluidity?

So-called vibe coding allows developers and non-developers to prototype software extremely fast. We know that. But what does this lead to? If it takes five minutes to generate an entire site, or, for the sake of argument, let’s confine our expectations to a single feature within an existing platform, which might take, what, 20 seconds? If that is true, what is preventing us from generating a different feature for every visitor? Customizing a feature for every customer?

Like matter, this once-solid block of software is now transforming into something fluid.

What Does This Mean for the Industry?

What we see is a never-ending appetite for new software solutions. The only thing stopping adoption is the price tag. We are not talking about monolithic advertising and marketing solutions; we are talking about small integrations that help just enough to get over the manual-labor curve.

AI allows us to build fast and efficiently, and to test and validate solutions quickly. This brings the price tag down and brings more customers in. So we say: bring it.

Practical Applications

In 2022, during the height of the Ukraine war crisis, many refugees were fleeing to Poland. This put a lot of strain on the system handling refugees. Volunteers had to meet people at train stations, highway stops, and bus stops to give directions to shelters and provide food and water.

A need arose. Partnering with a Polish-based NGO, we at C9 Group developed Voluntarius: a very simple volunteer management platform that enabled coordinated efforts to help refugees.

Once we had this product, we started looking into other NGOs. What we found surprised us. There was no goliath in the volunteer management space: just a couple of outdated systems in desperate need of modernization. This drove managers back to spreadsheets time and time again.

The reason? Due to the dynamic nature of volunteering, there can’t be a one-platform-fits-all solution. Either a system has too many features, with a large learning curve and a hefty price tag, or too few features that fail to meet real needs.

This is where AI can step in and help. It has never been easier to develop new features; we just need platforms that are flexible enough to allow for this kind of fluidity: dramatically driving down the cost of customizable software.

Conclusion

Peeking into the future, we see software becoming cheap, widely available, and (most importantly) extremely adaptable, customizable, and fluid.

Welcome to the future.