It is, I think, a common feeling among Echo/Alexa users. Now that people are getting used to the amazing understanding capabilities of ChatGPT and the likes, it probably increases the frustration level because you get a hint of how good it could be.
I believe it boils down to two main issues:
- The narrow AI systems used for intent inference have not scaled with the product features.
- Amazon is stuck and can't significantly improve it using general AI due to costs.
The first point is that the speech-to-intent algorithms currently in production are quite basic, likely based on the state of the art from 2013. Initially, there were few features available, so the device was fairly effective at inferring what you wanted from a limited set of possibilities. Over time, Amazon introduced more and more features to choose from, but the devices didn't get any smarter. As a result, mismatches between actual intent and inferred intent became more common, giving the impression that the device is getting dumber. In truth, it’s probably getting somewhat smarter, but not enough to compensate for the increasing complexity over time.
The second point is that, clearly, it would be relatively straightforward to create a much smarter Alexa: simply delegate the intent detection to an LLM. However, Amazon can’t do that. By 2019, there were already over 100 million Alexa devices in circulation, and it’s reasonable to assume that number has at least doubled by now. These devices are likely sold at a low margin, and the service is free. If you start requiring GPUs to process millions of daily requests, you would need an enormous, costly infrastructure, which is probably impossible to justify financially—and perhaps even infeasible given the sheer scale of the product.
My prediction is that Amazon cannot save the product, and it will die a slow death. It will probably keep working for years but will likely be relegated by most users to a "dumb" device capable of little more than setting alarms, timers, and providing weather reports.
If you want Jarvis-like intelligence to control your home automation system, the vision of a local assistant using local AI on an efficient GPU, as presented by HA, is the one with the most chance of succeeding. Beyond the privacy benefits of processing everything locally, the primary reason this approach may become common is that it scales linearly with the installation.
If you had a cloud-based solution using Echo-like devices, the problem is that you’d need to scale your cloud infrastructure as you sell more devices. If the service is good, this could become a major challenge. In contrast, if you sell an expensive box with an integrated GPU that does everything locally, you deploy the infrastructure as you sell the product. This eliminates scaling issues and the risks of growing too fast.