AI gurus seem to be running out of content ideas these days. To make up for this loss, they often come up with new, “brilliant” ideas to fix problems that do not really exist.
For example:
- Replacing n8n or Make.com with Claude Code.
- Self-hosting n8n instead of using n8n Cloud.
- Creating Proxy Servers via Claude Code.
- Hosting LLM Models Locally.
Content creators need constant novelty. Once you've explained n8n basics, you need increasingly elaborate setups to keep publishing.
Many content creators don’t really care about the practical utility. They just want more views.
There's a seductive appeal to building everything yourself.
Deploying your own serverless functions, replacing visual automation tools with custom Python scripts, and hosting infrastructure you fully control sounds empowering.
But for most AI developers and automation builders, this path leads to overengineering, where technical complexity outweighs practical benefit.
What is AI Overengineering?
Overengineering occurs when you build a more complex solution than your problem requires. It often disguises itself as optimisation, cost savings, or future-proofing.
The fear of missing out often forces many AI developers to stop what they are doing and overengineer a new trick.
In voice AI and automation development, overengineering typically manifests as:
- Replacing working visual automation tools with custom code.
- Self-hosting platforms that work perfectly fine as managed services.
- Building infrastructure to save small amounts of money.
- Adding layers of abstraction that require ongoing maintenance.
- Optimising for problems you don't actually have.