My Smart Coffee Machine Refuses to Make Coffee

Mornings are supposed to start with a cup of coffee and a boost of energy. But for one tech reporter at The Verge, it has become a daily lesson in the quirks of AI. She walked into her kitchen, addressed her Bosch coffee machine with Alexa support, and said: “Brew me a cup of coffee.” No complicated requests, no improvisation—just a simple, pre-programmed command. And yet, she was denied. Not once, but repeatedly. Since upgrading to Alexa Plus, Amazon’s generative AI voice assistant, these rejections have become part of her morning routine.

It’s almost laughable: in 2025, AI can write papers, code software, chat with people, even teach—but a simple “brew coffee” command can still trip it up. Online communities are filled with similar stories, from turning on lights to playing music and setting timers—tasks that should be trivial have become sources of frustration.

Why does this happen? The answer is LLMs, or large language models. They introduce inherent randomness. They understand nuances, generate rich responses—but at the cost of predictability. For tasks requiring immediate, repeatable, and error-free control—like brewing coffee—this creativity becomes a problem. Traditional voice assistants, by contrast, are predictable: they recognize keywords, fill parameters, and consistently execute commands.

Amazon and Google have tried integrating LLMs with smart home APIs to address this, but even slight deviations can cause failure. So, it’s no surprise when your coffee machine refuses to cooperate.

In theory, it’s possible to make new assistants as reliable as old ones, but it requires massive engineering and fallback measures. In reality, with limited resources and stronger incentives for experimentation and profit, the simplest approach is to deploy technology in the real world and let it self-correct. We are all long-term beta testers of AI.

Why continue pushing generative AI? The answer is potential. Agentic AI can understand complex tasks, generate execution logic dynamically, and do what old, rule-based assistants cannot. Traditional systems are single-command executors—they cannot decompose tasks, understand goals, or create new action paths.

Community discussions show that while basic commands still fail, new assistants excel at handling complex instructions. Adjusting lights, temperature, or music—multi-step routines—are easier to set up than in the old app interfaces. Camera alerts are smarter: “Unknown face at the door, but didn’t enter the yard,” replacing vague “motion detected in backyard” messages.

The takeaway is clear: the problem isn’t AI itself—it’s whether it’s placed in the right context. Generative AI isn’t failing; it’s just being asked to do things it wasn’t designed for. Today, the boundary is still being defined—but it’s one to watch with excitement.