preface
Modern language models feel like magic in a demo. You type a prompt, and within seconds, you get an answer that would have seemed like science fiction a few years ago. Then you try to ship that same capability to real users, and the magic starts to leak. The model invents a citation. It follows an instruction it should have refused. It works flawlessly for a week and then quietly degrades on a Monday morning when nobody is watching.
I have lived on both sides of that gap. Over the years, I’ve built AI, machine learning, and search systems at LinkedIn; worked on natural-language processing (NLP) and generative AI at Element AI; worked on backend and infrastructure at Shopify; and co-founded Persana AI, where our systems powered go-to-market work for thousands of teams. Across all those years, the same lesson kept repeating: the hard part is almost never getting a model to do something impressive once; the hard part is getting it to do the right thing reliably, for every user, on the thousandth ordinary day after launch.
The numbers back this up. Industry studies have found that the vast majority of generative AI pilots never deliver real return on investment. Again and again, teams hit the same walls: hallucinations, flaky outputs, brittle tool calls, weak evaluations, and safety and fairness problems that surface only when real people are involved. The technology is extraordinary; the engineering discipline around it is still catching up.