AI development can be expensive—and unpredictable. In this TMOT episode, we unpack why AI projects often exceed budget expectations and what companies can do about it. From scope creep to training data issues, the hidden pitfalls are real.
Learn how to set better expectations and why working with the right development team can make all the difference.
AI development tools are evolving fast. There are now platforms that promise to scope, spec, and even build your software, sometimes in minutes. Sounds amazing, right? But here's the thing. There's often a big gap between what's promised and what's actually possible. And we're going to talk about it in today's version of 2 minutes on Tech, brought to you by Art and Logic.
Well, you've probably seen the ads. Custom software in minutes. "Oh, just bring us your idea. Just tell us what you wanted to build and poof, presto. AI will make it happen to make all your dreams come true." Yeah. Well, uh, is that really actually possible? I sure have played with a ton of them. And while sometimes the interface is pretty cool, the software seems a long way from being finished. I mean, a long way. So, I wouldn't just call it snake oil, but if it slithers and it hisses and it's got scales, I mean, you do the math. Slithery little snake. A little slithery snake.
And I'm not saying that AI probably won't get there very soon, because it most likely will, but it's not ready yet. And even the AI-generated estimates we run into, they just dramatically understate the timelines. And it's not that the tools are useless, it's just that they often don't account for real-world edge cases, technical constraints, or even business-specific needs. You get an optimistic blueprint, but not the engineering depth to back it up. That disconnect can mislead stakeholders, derail budgets before a single line of code is ever written.
Listen, AI can be a powerful accelerant, but it's not a substitute for expertise, context, and judgment. Scoping software and delivering estimates still requires asking hard questions like "What's the user actually trying to do?", "How will the system evolve?", and "What happens when things go wrong?" AI doesn't always know what it doesn't know. You still need humans for that.
So before you go greenlighting that project based on an AI-generated spec, just take a second, talk to someone who's shipped complex software before, validate the assumptions, pressure test the plan. Because building software isn't just about speed, it's about stability, security, and scalability. And getting it wrong early can be expensive. This has been two minutes on tech brought to you by Art and Logic.