Why Solving Real Problems Beats Chasing AI Tech Hype
Hey fellow devs!
Have you ever felt the pressure to shove artificial intelligence into your application just because everyone is raving about it? You aren’t alone; our entire industry is currently experiencing a mix of pure excitement and severe FOMO. In a recent live session of Out of the Box Developer, we sat down with Fabiana Piaza, a seasoned global business executive, to talk about digital transformation and what it actually takes to build impactful software with AI today.
Let’s break down why focusing on real problems—rather than shiny technology—is the ultimate superpower for software engineers right now.
The Temptation of “Solution-First” Development
As developers, we love experimenting with brand-new APIs and cutting-edge models. When a powerful tool drops, our immediate instinct is to ask: “What cool project can I build with this?” While that mindset is awesome for weekend tinkering, it can be fatal when building actual products or delivering enterprise software.
Far too often, teams end up creating a solution and then desperately searching for a problem that fits it. When you work backwards from technology, you risk spending weeks tweaking prompts and chaining models together, only to realize nobody actually needs what you built.
During our conversation, Fabiana captured this exact dilemma:
Fabiana: “The first big challenge is always to define the problem to solve, believe it or not. Everybody starts with different ways of thinking, and the more multi-functional you go in defining your problem, the harder it is. Even talking about AI—it’s a buzzword everywhere, and everybody wants to implement something with it—I always step back and say: ‘Okay, what’s the real customer problem we want to solve?’ We have to get everybody on the same page before a project even starts. It sounds simple, but it’s tough and often the most important step.”
Does this sound familiar in your daily standups? If your team is debating tech stacks before agreeing on user friction, you’re putting the cart before the horse.
Why Hobby Projects Fail in the Real World
We’ve all seen developers build incredible demos on social media. They string together vector databases, agent frameworks, and LLMs into slick user interfaces. Yet, a few months later, the application is abandoned with zero active users. Why does that happen so consistently?
I brought up this exact issue during our live discussion because it’s something I see constantly in developer communities:
Pedro: “Using AI without a clear purpose sounds simple, but it is key for the success of any project, big or small. Solving a problem is key to the success of absolutely anything. There are lots of people burning tokens crazily on personal projects, and tons of apps are being generated that solve nothing. People then complain, ‘Why is my app not successful?’ It’s simply because it’s not solving anything.”
When we burn through API credits without solving an acute user pain point, we’re not building a product—we’re indulging in a technical hobby. There is nothing wrong with learning for fun, but if your goal is traction, adoption, or business impact, solving a painful problem must come first.
The Hidden Trap of High Budgets
Interestingly, this problem doesn’t vanish when you move from indie hacking to corporate enterprise projects. In fact, having more funding can actually make things worse. Fabiana explained why having plenty of cash can be a double-edged sword:
Fabiana: “The higher the budget you have, the more hyperfocused you need to be on a real problem to solve. That’s when you have more flexibility to have what I call ‘hobby projects’ instead of really targeted ones that bring a payback. The ultimate discussion is: how much effort, energy, and budget are you putting into AI, how much is it really converting, and how do you measure it? The higher the budget, the trickier it gets to narrow down to the real problem.”
When teams have large budgets, they can easily afford to build flashy prototypes that look impressive during executive demos but fail to deliver any measurable return on investment.
How to Uncover Genuine Developer and Customer Pain Points
So how do we break out of this hype cycle and identify problems that genuinely justify an AI-driven solution? You don’t need a business degree to do this; you just need disciplined developer empathy.
Here is a practical checklist you can use before writing your next line of code:
1. Listen to the “Voice of the Customer”
Your customer isn’t always an external buyer. If you’re building developer tooling or internal platforms, your customer is the engineer sitting next to you. Observe where people spend mundane hours doing repetitive chores. Are they copy-pasting data across spreadsheets? Are they wrestling with poorly structured configuration files? Where is the frustration highest?
2. Put Yourself Directly in the User’s Shoes
Before drafting an architecture diagram, walk through the existing user workflow yourself. Feel the friction firsthand. If a task takes five manual clicks and ten seconds of thought, an AI agent with five seconds of latency might not even be an improvement.
3. Align Cross-Functional Stakeholders Early
Disagreements kill software projects. If the product manager thinks the goal is speed, the engineer thinks the goal is zero hallucinations, and the executive thinks the goal is cost reduction, the project will stall. Get everyone in the same room and write down the single problem statement in plain English.
4. Ask: “Can Simple Code Solve This Better?”
Don’t reach for a generative model when a deterministic script, regular expression, or standard database query gets the job done faster, cheaper, and with 100% reliability. Reserve AI for problems where unstructured data, semantic reasoning, or human nuance make classic coding impractical.
Practical Takeaways for Your Next Sprint
If you want to stand out as a software engineer in this new AI era, stop positioning yourself as just someone who knows how to call model APIs. Position yourself as someone who knows how to evaluate user problems and apply the simplest, most effective tool to fix them.
Here is what you can start doing today: – Challenge requirements constructively: When someone asks you to “add an AI feature,” ask them what user KPI or metric that feature is expected to improve. – Run small, bounded experiments: Don’t build a massive end-to-end pipeline on day one. Test a tiny prompt or micro-service on a real subset of user data. – Keep cost and latency top of mind: Always consider token costs, inference latency, and maintenance overhead before declaring a prototype production-ready.
Remember: technology changes fast, but user needs remain grounded in saving time, cutting stress, and getting jobs done reliably.
Watch the full video here.
What are your thoughts on this? Have you ever caught yourself building a feature just for the sake of using AI? Share your experiences in the comments below!
Happy coding!
Pedro Cavalero
