The Impact of AI on Smart Homes in 2025

This morning, I asked my Alexa-enabled Bosch coffee machine to brew a cup of coffee, but instead of getting my usual fix, it informed me that it couldn’t do that. Ever since I upgraded to Alexa Plus, Amazon’s AI-driven voice assistant, it has struggled to execute my coffee routine, offering a new excuse each time I make the request.

Here we are in 2025, and AI still can’t consistently manage my smart home. I’m starting to doubt if it ever will.

The promise of generative AI and large language models simplifying the smart home experience is enticing. The idea of a “new intelligence layer” that could create a more proactive, ambient living space is appealing. However, this year has made it clear that we’re far from realizing that vision. Instead of the reliable assistants we once had, we now have “smarter” versions that, while better at conversation, often fail to perform basic tasks like controlling appliances or turning on lights. I want to understand why.

This isn’t the future we envisioned.

Back in 2023, during a conversation with a tech expert, I first got excited about the potential of generative AI and large language models to enhance the smart home experience. The expert, who was leading the development of a new Alexa, described how this upgraded assistant would understand commands regardless of phrasing. What caught my attention was the promise that this new Alexa could leverage its knowledge of smart home devices and the numerous APIs connected to them, providing the context needed to simplify smart home management.

From device setup to controlling features and managing interactions between devices, a more intelligent assistant seemed poised to make it easier for tech enthusiasts and everyday users alike to enjoy the benefits of a smart home.

Fast forward three years, and the most significant upgrade we’ve seen in smart home AI is AI-generated descriptions for security camera alerts. While useful, it’s hardly the groundbreaking change I had hoped for.

These new smart home assistants aren’t complete failures. I appreciate many aspects of Alexa Plus; I even named it my top smart home software of the year. It’s more conversational, understands natural language better, and can answer a wider range of questions than its predecessor.

While it sometimes falters with basic commands, it can handle more complex requests; for instance, saying “I want it dimmer in here and warmer” will adjust the lights and raise the thermostat. It’s improved at managing my calendar, assisting with cooking, and other home-related tasks. Setting up routines via voice commands is a significant upgrade over the old Alexa app, even if executing those routines isn’t as reliable.

Google has promised similar advancements with its Gemini for Home upgrade for smart speakers, but the rollout has been slow, and I’ve only experienced limited demos. I did test Gemini for Home’s feature that summarizes events at home using AI-generated text from Nest camera footage, but it was wildly inaccurate. As for Apple’s Siri, it remains stuck in the past and shows no signs of catching up anytime soon.

The issue is that these new assistants lack the consistency of their predecessors when it comes to controlling smart home devices. While the old Alexa and Google Assistant (and the current Siri) could generally turn on the lights when asked, as long as you used the right terms, today’s upgraded versions struggle with basic functions like lighting, timers, weather reports, music playback, and executing routines that many of us rely on for our smart homes.

I’ve noticed this inconsistency in my own testing, and online forums are filled with users sharing similar frustrations. Both Amazon and Google have acknowledged the challenges their revamped AI assistants face in reliably performing fundamental tasks. This issue isn’t limited to smart home assistants; even advanced models like ChatGPT can struggle with basic tasks like telling time or counting.

So, what’s going on, and will it ever improve? To get some insight, I spoke with two experts in human-centric artificial intelligence who have experience with smart home systems. My takeaway is that while it’s technically feasible to make these new voice assistants perform as well as the old ones, it requires significant effort—effort that many companies may not be willing to invest.

With limited resources in this field and a plethora of opportunities for more exciting (and profitable) projects, experts suggest that companies are opting to deploy these technologies in the real world and let them evolve over time. This likely explains why Alexa Plus and Gemini for Home are still in “early access” phases, effectively making us all beta testers for the AI.

The downside is that it may take a while for improvements to materialize. Research by a computer science professor has shown that newer models of smart home assistants tend to be less reliable. “They’re more conversational and people enjoy interacting with them, but they don’t perform as well as the previous versions,” he notes. “Tech companies have always aimed to release products quickly, gather data, and improve them over time. So, while we might see better models in a few years, it comes at the cost of users struggling with them in the meantime.”

The core issue seems to be that the old and new technologies don’t integrate well. To create their new voice assistants, companies like Amazon, Google, and Apple have had to discard the old systems and build entirely new ones. However, they quickly realized that these new large language models (LLMs) weren’t designed for the predictability and consistency that their predecessors excelled at. “It wasn’t as straightforward an upgrade as everyone initially thought,” says an expert in the field. “LLMs understand much more and can interpret commands in various ways, which can lead to mistakes.”

Essentially, LLMs aren’t built to perform the same command-and-control functions as earlier voice assistants. “Those older assistants are what we call ‘template matchers,’” the expert explains. “They look for specific keywords and know to expect a limited set of follow-up words.” For example, if you say “Play radio,” they anticipate a station code next.

In contrast, LLMs introduce a level of randomness. Asking the same question multiple times may yield different responses. This variability is part of their appeal, but it also means that when you ask your LLM-powered assistant to do something you requested yesterday, it might not respond the same way. “This randomness can lead to misunderstandings of basic commands because they sometimes overthink things,” the expert adds.

To address this, companies like Amazon and Google have developed methods to integrate LLMs with the APIs that power our smart homes. However, this has created a new challenge.

“Now, LLMs must compose function calls to APIs, which requires them to work much harder to create the correct syntax,” the expert explains. Unlike the old systems that simply waited for a keyword, LLM-powered assistants must generate an entire code sequence that the API can recognize. “They have to keep all that in memory, which is another area where mistakes can occur.”

This is a technical way of explaining why my coffee machine sometimes refuses to brew coffee or why you might struggle to get Alexa or Google’s assistant to perform tasks it used to handle effortlessly.

So, why did these companies abandon a working technology for something less reliable? Because of the potential for greater capabilities. A voice assistant that can understand natural language and respond accordingly is far more powerful than one limited to specific commands.

“What all the companies behind Alexa, Siri, and similar assistants really want is the ability to chain services,” the expert explains. “They aim for a general understanding of language that can grasp complex relationships between tasks and how they’re expressed in speech. They want to create dynamic sequences of actions on the fly.” This is what could make them truly agentic.

The question remains whether the broader range of possibilities offered by this new technology is worth the trade-off in accuracy compared to the previous models.

One potential solution is to utilize multiple models to power these assistants. Google’s Gemini for Home, for instance, consists of two separate systems: Gemini and Gemini Live. The goal is for the more advanced Gemini Live to eventually take over, but for now, the more constrained Gemini for Home is in charge. Amazon employs a similar strategy with multiple models to balance its capabilities. However, this approach has led to inconsistencies and confusion in our smart homes.

Experts note that no one has yet figured out how to train LLMs to know when to be precise and when to embrace randomness, meaning even the more controlled LLMs can still make errors. “If you wanted a machine that was never random, you could restrict it,” one expert states. But that would sacrifice the conversational abilities and creativity that make these assistants appealing—qualities that companies like Alexa and Google are promoting.

These challenges in smart home deployment could signal broader issues for the technology. If AI can’t reliably turn on the lights, why should we trust it with more complex tasks? “You have to walk before you can run,” the expert emphasizes.

However, tech companies are known for their tendency to move quickly and take risks. “The narrative around language models has always been about taming LLMs,” the expert explains. “Over time, they become more reliable and trustworthy, but we keep pushing the boundaries where they struggle.”

Despite the current limitations, there is hope for a future with more reliable AI assistants. “I’m not sure if we’ll ever reach AGI, but I believe we’ll see improvements in reliability over time.” The real question for those of us dealing with these unreliable AIs in our homes today is whether we’re willing to wait and what the cost will be for our smart home experience in the meantime.