Wie KI das Smart Home im Jahr 2025 zerstörte | Die Einführung generativer KI-Assistenten in unseren Smart Homes war vielversprechend; Stattdessen fällt es ihnen schwer, das Licht einzuschalten

I’m so pessimistic about AI. It seems like it will turn everything into shite.

For every positive aspect it has three substantial downsides. I think first it’ll just break the internet, then it’ll steal our livelihoods. And in the end it’ll try to kill us too.

Fuck AI. It’s cancer.

Some issues noted by the author:

>The potential for generative AI and large language models to take the complexity out of the smart home, making it easier to set up, use, and manage connected devices, is compelling. So is the promise of a “new intelligence layer” that could unlock a proactive, ambient home.
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>But this year has shown me that we are a long way from any of that. Instead, our reliable but limited voice assistants have been replaced with “smarter” versions that, while better conversationalists, can’t consistently do basic tasks like operating appliances and turning on the lights. I want to know why.
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>The problem is that the new assistants aren’t as consistent at controlling smart home devices as the old ones. While they were often frustrating to use, the old Alexa and Google Assistant (and the current Siri) would generally always turn on the lights when you asked them to, provided you used precise nomenclature.
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>Today, their “upgraded” counterparts struggle with consistency in basic functions like turning on the lights, setting timers, reporting on the weather, playing music, and running the routines and automations on which many of us have built our smart homes.
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>Why is this, and will it ever get better? To understand the problem, I spoke with two professors in the field of human-centric artificial intelligence with experience with agentic AI and smart home systems. My takeaway from those conversations is that, while it’s possible to make these new voice assistants do almost exactly what the old ones did, it will take a lot of work, and that’s possibly work most companies just aren’t interested in doing.
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>Considering there are limited resources in this field and ample opportunity to do something much more exciting (and more profitable) than reliably turn on the lights, that’s the way they’re moving, according to experts I spoke with. Given all these factors, it seems the easiest way to improve the technology is to just deploy it in the real world and let it improve over time. Which is likely why Alexa Plus and Gemini for Home are in “early access” phases. Basically, we’re all beta testers for the AI.
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>Basically, LLMs just aren’t designed to do what prior command-and-control-style voice assistants did. “Those voice assistants are what we call ‘template matchers,’” explains Riedl. “They look for a keyword, when they see it, they know that there are one to three additional words to expect.” For example, you say “Play radio,” and they know to expect a station call code next.
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>LLMs, on the other hand, “bring in a lot of stochasticity — randomness,” explains Riedl. Asking ChatGPT the same prompt multiple times may produce multiple responses. This is part of their value, but it’s also why when you ask your LLM-powered voice assistant to do the same thing you asked it yesterday, it might not respond the same way. “This randomness can lead to misunderstanding basic commands because sometimes they try to overthink things too much,” he says.
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>These struggles in its deployment in the smart home could be a harbinger of broader issues for the technology. If AI can’t turn on the lights reliably, why should anyone rely on it to do more complex tasks, asks Riedl. “You have to walk before you can run.”
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>But tech companies are known for their propensity to move fast and break things. “The story of language models has always been about taming the LLMs,” says Riedl. “Over time, they become more tame, more reliable, more trustworthy. But we keep pushing into the fringe of those spaces where they’re not.”

The continuing failures of these LLMs to operate reliably in the domestic environment doesn’t inspire confidence in the companies that are looking to push these technologies out to more mission critical areas like moving vehicles and power plants. Maybe tech companies should return to beta testing internally and fixing bugs before shipping devices, rather than rely on the public to do so.

You don’t need AI to turn on the lights but inspecting your cameras is a million times better than the old object detection method that was used

The Cloud broke the smart home. „AI“ made it more broken.

Not sure if it’s related to AI, but my home devices have taken a dumb in quality this past year. Asking basic tasks like playing music, setting a timer, searching something it just does wrong. Not sure if it’s just lack of care of the software updates, different priorities or AI coding but it’s frustrating

So I have a different perspective. I’ll give one example. There are certain natural language commands that never worked for me. „Turn off all the lights instead of the bedside lamp.“ was one. With the Gemini beta, this command now works. I agree, an LLM is not needed for this to be functional (as a simple logic based model would work fine), but they just never did it. This keeps them from having to manually maintain these patterns, which Google is notoriously bad at.

More moving parts means more things to go wrong. What we have for „AI“ right now is a colossal mess of moving parts.

Any dev that is competent (who isn’t just going along with their boss) will tell you LLMs absolutely should not be used for this edge case

But, the executive driven lies and misunderstanding of what LLMs can and should do usually wins out

They are not trying to improve home automation, to me they are trying to phase out without any legal notice

Oh no, I can’t do the thing I don’t want to do using the thing I don’t want to use.

Didn’t break mine because I’m not stupid enough to put AI near my implementation. If I ever do, it will be a local deployment that I can train and control myself.