I’ll probably regret writing this. At the very least, I’ll cringe reading it in a few months. But here we are.
Lately, we’ve been getting a wave of client requests asking us to evaluate software they built using AI tools. These aren’t engineers. These are business folks using increasingly powerful AI products to try and build functioning systems. And to be completely honest, the results are both impressive and a bit alarming.
People are building whole applications on their own. Backends, frontends, user interfaces, even some database logic. Sometimes they even look good. These are smart people who don’t know how to code but have managed to produce working systems.
The problems show up immediately when we start reviewing the internals. The code is usually a mess. In many cases, it would be extremely difficult to maintain or extend. And if you need to move that code from the platform it was created in to a cloud provider like AWS, you’re going to hit a wall. These platforms wrap everything in layers of scaffolding that make portability a nightmare.
Security is worse. We’ve found plaintext credentials scattered across files. We’ve seen SQL injection vulnerabilities that shouldn’t even be possible in modern frameworks. We’ve seen structural issues that would get flagged in a freshman CS class.
Despite all that, what people are creating are legitimate prototypes. They’re functional. They run. But when you’ve put a few weeks into building something, and you show it to a software engineer, it’s hard to hear that your shiny new thing needs to be rebuilt from scratch.
I want to be clear. I am not anti-AI. Almost everyone at my company uses AI tools every day. We use Copilot, Cursor, ChatGPT, Claude, and more. We build out frontends with tools like v0 and Lovable. These tools have changed how we work.
Some of our engineers report productivity improvements of 30 to 40 percent. That’s not a rounding error. That is a major shift. But they are still writing the code. They are reviewing it. They are checking for performance, clarity, security, and maintainability. They are not letting the tools decide architecture. They are using AI like they use autocomplete or linters, but with more power behind it.
This is also where expectations need to be adjusted. These systems will not save you 90 percent on development. They will not let you skip engineering altogether. But if they save you 30 percent, that’s a real gain. Imagine you’re building a house. The general contractor says it’s going to be $500,000. You tell them you already did the blueprints, filled out all the permits, and figured out the site plan using some AI tools. If they came back and said, “Alright, I’ll knock 30 percent off,” that would be the best deal of your life. That’s where we are today with AI-generated software. A solid start. A real value. Not a replacement.
For me personally, AI has made it fun to write code again. I haven’t been a working programmer in over a decade, and most modern toolchains are enough to scare me off. Now, with the right assistance, I can build something without getting stuck on Docker configs and dependency mismatches. That’s a big deal.
In the startup world, AI-first development is everywhere. Most of the current Y Combinator batch is using AI tools to write the bulk of their code. But those teams are highly technical. These are engineers using better tools, not tools replacing engineers.
So for non-developers using AI to build products, here are three things you should keep in mind:
- These tools are great for building prototypes. If you build something yourself, you will understand it better and will be a better partner to your engineering team. That matters.
- These tools can help you build usable frontend components. You probably won’t want to go live with them, but they can get you close enough to work with a real development team.
- If your app is small, non-critical, doesn’t store sensitive data, and lives entirely in its native platform, you can probably keep it running. That’s fine for internal use or personal projects.
One day, you’ll be able to speak an app into existence and deploy it with a voice command. It will be fast, secure, and beautiful. But today, you still need an experienced software engineer to check your work before you send real data through it. That’s just where we are right now.
The upside is huge. We can now get experts from other domains to build working prototypes and test ideas without needing an engineering team on day one. That’s powerful. But if your product is going to handle sensitive data or support real users, bring in someone who knows what they’re doing. Not because the AI is bad. Because the stakes are high.
