Building Real AI Systems
What it actually takes to build AI systems that work in production, not just in demos.
Building production AI systems is fundamentally different from building demos. Here’s what actually matters:
Beyond the Demo
Most AI projects fail because they conflate prototype with product. A working demo isn’t a system—it’s a proof of concept with training wheels still on.
Real systems need:
- Reliability — They work when they’re supposed to, not just sometimes
- Degradation — When they fail, they fail gracefully
- Observability — You know what they’re doing and why
- Cost discipline — They don’t burn your budget on edge cases
The Hidden Complexity
The AI model is maybe 20% of the work. The other 80% is plumbing:
- How do you handle errors?
- What do you do when the API is down?
- How do you version your prompts?
- Can you rollback a bad deployment?
- Do you have a way to test before pushing?
This is where most projects break. The engineers who know ML don’t know systems. The engineers who know systems haven’t built with AI.
What I’ve Learned
Building Fern, Frosted, and Codius taught me that the bottleneck isn’t the model—it’s the infrastructure around it. GPT-4 is incredible, but if your system can’t handle a 30-second API timeout, it doesn’t matter how smart the model is.
The teams winning with AI aren’t the ones with the fanciest prompts. They’re the ones with boring, solid engineering practices applied ruthlessly.