The future of the smart home arrived years ago – and it doesn’t work as promised. Despite the hype around generative AI, today’s voice assistants are less reliable than their predecessors, struggling with even basic tasks like turning on lights or brewing coffee. The core problem isn’t a lack of intelligence, but a fundamental mismatch between how these new AI systems operate and what smart homes need them to do.
The Illusion of Progress
In 2023, Amazon’s Dave Limp hinted at a breakthrough: a voice assistant that understood context, seamlessly integrated with smart devices, and simplified home automation. Fast-forward to 2025, and while assistants like Alexa Plus and Gemini for Home sound smarter, they often fail at core functions. The current “upgrades” prioritize conversational ability over consistency. Users report that their devices can’t reliably execute commands, even after years of setup.
The situation is so widespread that tech companies acknowledge the issue. The problem isn’t limited to smart homes: even ChatGPT occasionally struggles with basic logic. This failure isn’t due to laziness; it’s a consequence of a flawed approach.
Why AI Can’t Get It Right
The shift from older, “template-matching” voice assistants to newer LLM-based systems created a fundamental disconnect. Older assistants were rigid but dependable; they executed precise commands predictably. LLMs, while more versatile, introduce randomness. The same query can yield different results each time, making basic tasks unreliable.
“LLMs just aren’t designed to do what prior command-and-control-style voice assistants did,” explains Mark Riedl, a professor at Georgia Tech. These new systems struggle to consistently perform actions that older models handled with ease. The LLMs must now construct entire code sequences for APIs, introducing more points of failure.
The Cost of “Intelligence”
Tech companies aren’t abandoning the old technology; they’re chasing a more ambitious goal: an agentic AI that understands natural language and chains tasks dynamically. This requires sacrificing reliability in the short term for the potential of far greater capabilities.
Dhruv Jain, director of the University of Michigan’s Soundability Lab, sums it up: “The question is whether … the expanded range of possibilities the new technology offers is worth more than a 100 percent accurate non-probabilistic model.” The current approach is essentially beta testing in real-world homes.
What’s Next?
Companies are experimenting with hybrid models, like Google’s Gemini Live, to balance power and precision. But even these solutions remain imperfect. The underlying issue is that LLMs haven’t been adequately trained to distinguish between situations demanding absolute accuracy and those where flexibility is valued.
The failures in smart home AI raise broader questions about the technology’s readiness for more critical applications. If AI can’t reliably turn on lights, what confidence can we have in its ability to handle complex tasks? The path forward involves taming the randomness of LLMs, but at the cost of conversational depth.
This broken promise of the smart home serves as a cautionary tale: moving fast and breaking things isn’t always progress. Tech companies must decide if the potential of advanced AI outweighs the immediate frustration of unreliable devices. For now, many users are left with a smarter, yet more frustrating, smart home experience.
