Whatever your stance on the current state of artificial intelligence, it’s snowballed into a force that’s reshaping how we approach work. It’s healthy to be skeptical; after all, there’s no
AI launch plans often start with a familiar list: GPUs, model size, software stack, and budget. Yet the date that matters most, the moment an AI system actually goes live,
Most companies don’t fail with AI because the technology doesn’t work. They fail because they expect it to work without changing anything. Over the last two years, you’ve probably seen
You’ve probably felt this shift already. What started as “just add a model call here” turns into something your entire system quietly depends on. Latency budgets change. Observability breaks. Product
You shipped the model. It passed red-teaming. The prompts are sanitized, outputs are filtered, and access is gated behind your standard auth layer. On paper, your AI stack looks “secure.”
The first version of an AI feature rarely looks dangerous. It is a thin wrapper around an API, a prompt in code, a vector store standing off to the side,
As reflected in the rising cost of graphics processing units (GPUs), today’s builders of artificial intelligence (AI) infrastructure have made a clear, but faulty assumption: more and faster GPUs will
Artificial intelligence creates code at staggering speeds for development teams. This rapid generation introduces unexpected problems with accuracy and security. Programmers face mounting pressure to verify these automated outputs before
“AI will take our jobs!” has become a classic joke nowadays. While many don’t take it seriously, some industries are already approaching this technology with caution. In sales, where trust,