Reliable Enterprise AI

The hype around large language models is real, loud, and often counterproductive. Here's what two decades of building software teaches you about separating what AI can actually do from what the demos suggest.

The demo works. The production system doesn't. That gap isn't a model problem — it's an architecture problem. This is what enterprise teams consistently underestimate when they ship AI for the first time.

Before I choose a framework, evaluate a model, or design a pipeline, I'm asking questions that have nothing to do with capability. They have everything to do with what happens when the system is wrong. The teams that build reliable enterprise AI are not the ones that move fastest — they're the ones that answer these questions first.

I've been skeptical of technology hype cycles for most of my career. This one is different, not because the hype is accurate, but because the underlying capability is real and the enterprise engineering problems it creates are genuinely unsolved. Here's why engineers with production experience should be paying attention, and what I'm watching in 2024.