The Rise of Single-Use Software

It was one of those late nights where everything seemed under control until it wasn't. Our order-processing system, vital for night-time nightclub ticket sales, hit an unexpected snag. I had tools at my disposal. But this problem was unique, demanding a solution our pre-built tools couldn't immediately provide.

It was in those early days of ChatGPT when we were all discovering what it could do. In that moment, I was confronted with a choice: dive into a high-pressure coding frenzy or try something new.

The solution ChatGPT provided was a bespoke Ruby script, elegantly simple and perfectly suited to our specific problem. I ran the script once to fix the problem and then I threw it away.

You aren't going to need it

This incident was a practical lesson in the YAGNI principle — an acronym that stands for 'You Aren't Gonna Need It.' It's a bit like buying a bunch of bananas when you only ever eat apples; why stockpile something you're unlikely to use? YAGNI teaches us to create software features only when they're necessary, rather than investing in them 'just in case.'

That night, combined with the new coding capabilities of generative AI, I realized something bigger was happening. It's no longer just about the apps or tools we build in anticipation of problems. The YAGNI principle, now turbocharged by AI, leads to a new principle: Focus on solutions, not on tools. It's about being agile and responsive, crafting solutions in real-time as challenges arise. And, crucially for professional software developers, it will change how the market values software projects. It will also reduce demand for developers to invest value in big software projects.

Ephemeral solutions empower individuals

ChatGPT's "Advanced Data Analysis" and traditional tools like Excel represent distinct paths in the realm of data analysis. Picture a common task: you have a CSV file with sales data and your goal is to calculate aggregate revenue over various time periods. Using Excel, your journey involves leveraging spreadsheet expertise to filter and compile the data.

There's an alternative though. Input that CSV into ChatGPT. Observe as it effortlessly crafts Python code, a bespoke solution engineered for your unique task. This approach bypasses the rigmarole of maintaining and updating code. It's a use-and-discard strategy, a transient yet effective solution in the fast-paced world of data analysis.

The debate between Excel and ChatGPT isn't about supremacy. There's a time and a place for each of the two ways. But the costs of those ways are different: Excel demands a significant investment in learning and mastery, while AI-driven alternatives like ChatGPT offer a more direct route. They allow individuals to concentrate on the problem at hand rather than the intricacies of the tool used. An obvious win when your goal is to solve a problem.

Ephemeral solutions drive organizational efficiency

In the world of technology and business, the YAGNI (You Aren't Gonna Need It) principle has traditionally guided software development, advocating for creating only what's necessary at the moment. But what if we extend this principle beyond code, applying it to the very infrastructure and tools that businesses rely on? The YAGNI principle is revolutionizing not just software development but also the broader landscape of business investments in both hardware and software.

Cloud Computing: Just-in-time computer hardware expenses

Imagine running a business that relies on powerful computers to operate. Before the cloud computing revolution, you had to buy these computers in advance and accept the total cost of ownership. It's similar to purchasing a car – you pay a large sum upfront, even if you might not use it to its full capacity every day. And the sticker price is never the full price of owning and maintaining the car for years. In the technology world, this upfront investment is known as "Capital Expenditure," or CAPEX.

Now, think about cloud computing. It's like renting a car when you need it: Instead of owning and maintaining computers, you only pay for the computing power you use when you need it. This model is called "Operating Expenditure," or OPEX. It's been said that "cloud computing" simply means "someone else's computer," and that's really the whole point: You don't have to own the computer to use it.

Ephemeral solutions are just-in-time development expenses

In the high-stakes world of business software investments, one of the most daunting challenges has always been accurately predicting utilization. How much will we really use this? Is the commitment to an annual license from giants like Salesforce or Oracle justified by actual need? These questions have traditionally left business leaders in a quandary, often resulting in costly gambles on features or products that may see limited use.

Generative AI is emerging as a powerful ally in addressing this utilization estimation problem. By shifting the focus to creating single-use, just-in-time software tools, it allows businesses to bypass the risky bets associated with heavy software development projects or long-term licensing commitments.

With this AI-driven approach, companies won't need to overcommit to large-scale software projects or pricey annual licenses, basing their decisions on uncertain usage forecasts. Instead, they'll be able to adopt a more flexible strategy, generating specific solutions precisely when needed. This adaptability significantly lowers the risk of overinvestment in underutilized software features or products.

By mirroring the CAPEX-to-OPEX shift seen in cloud computing, generative AI gives businesses a paradigm shift towards greater business agility: staying nimble, investing in tailor-made tools on an as-needed basis, and making sure every software investment corresponds to a real, immediate need.

Cloud computing did this for hardware. AI is doing it for software.

Cloud computing opened doors to computing power for everyone from small businesses to individuals, breaking down the monopoly of large corporations. That same shift is now unfolding in software. Smaller teams and individual entrepreneurs are harnessing AI-driven tools for business innovation, bypassing the hefty financial burdens of traditional software development. This change enables a diverse group of innovators to undertake projects that were once financially out of reach.

Rethinking Applications: Adapting to On-Demand Solutions

As computers get better at providing solutions on demand, the role of traditional operating systems and applications invites reconsideration.

Consider the implications if tools like ChatGPT and its advanced data analysis were available during your initial forays into Excel. Would you have bothered to learn how to work with Excel formulas or pivot tables?

Conclusion: Solutions Over Tools

We're part of a shift in business software investment that echoes what cloud computing did for hardware. The guiding principle is clear: focus on solutions, not tools. Custom-fit, on-demand solutions are becoming the standard, and that's not just about access to technology — it's about making software investment track more closely with what people and businesses actually need.