The Tale of the Perfect Yet Impractical Solution
In a bustling tech startup called KathTech Inc. (not the real name), our team was working on a highly anticipated app—one that was meant to revolutionize the way people ordered bubble tea (not a real app). Each of us brought unique ideas to the table, but there was one person who stood out: Adrian the Architect. Known for his brilliant mind, Adrian had a reputation for coming up with ideal, flawless technical solutions. The kind of solutions that made you go, “Wow, that’s genius!” —until you realized they would take three years to build.
One day, we were assigned a task: users were clamoring for a simple feature to customize their drinks—like choosing ice level and sugar percentage. Adrian took it upon himself to brainstorm, and boy, did he deliver.
“Guys,” Adrian began, with a dramatic pause, “we’re going to build a machine-learning algorithm that predicts the perfect drink customization for each user based on their past orders, current mood, and even weather conditions. It’ll be powered by a custom-built AI model trained on terabytes of data. Users won’t even have to think; the app will just know.”
The team was in awe. Adrian’s solution sounded incredible—like something out of a sci-fi movie. But then I raised my hand.
“Adrian, I love your vision,” I started diplomatically, “but we’re dealing with users who just want to pick their sugar level today. Training an AI model? Building a recommendation engine? That’ll take months, if not years. By the time we’re done, our users might have switched to making their own bubble tea at home!”
The room fell silent. I could tell Adrian was a bit deflated. “So, what do you suggest, then?” he asked, arms crossed.
“Well,” I said, “why don’t we start simple? Let’s create a basic feature where users can manually select their ice level and sugar percentage. It doesn’t need AI or machine learning—just a few dropdown menus. It’ll deliver value immediately, and users will appreciate having the option. Once it’s up and running, we can collect feedback and, then, look into fancier solutions like your AI model.”
In the world of software development, it’s easy to get lost in the allure of ideal solutions—perfect, elegant, but often impractical in the short term. Being a pragmatic programmer means focusing on delivering value first, even if the solution is simple or imperfect. It’s about understanding that progress is better than perfection, and that sometimes, a few dropdown menus can pave the way for game-changing AI.
So, the next time someone proposes a solution that sounds more like a PhD thesis than a quick win, remember: a bubble tea drinker just wants their 50% sugar level today. The perfect AI can wait.