In recent years, there has been growing frustration among users of voice assistant devices like Google Home, Amazon Alexa, and Apple Siri. What once seemed like intuitive, reliable voice assistants are now sources of aggravation. Users (including myself) report increasingly frequent problems, such as misunderstood commands, inconsistent performance, and confusing responses to even basic requests.[1] This degeneration in functionality has raised a critical question: Why are voice assistants lagging behind when artificial intelligence (AI) is making big strides in many areas?
In this week's New Tech Tuesday, we explore the declining performance of ubiquitous voice assistants and consider some of the key issues at the root of this decline.
One of the biggest culprits behind voice assistants' declining performance is the accumulation of technical debt. Technical debt occurs when developers prioritize rapid feature releases over proper system maintenance and optimization. This leads to complex, bloated software that is difficult to update or debug without breaking functionality. Related, software fragmentation is a result of multiple poorly integrated changes over time, further contributing to this decline and the accumulation of more technical debt.[2]
Over the last few years, AI chatbots such as ChatGPT, Gemini, and CoPilot have significantly advanced natural language processing (NLP), image recognition, and predictive analytics. The accelerated proliferation of these chatbots has led to quick acceptance across horizontal market sectors, including the smart home. Most of these chatbots offer edge-based options that run on current hardware, enabling them to quickly respond to commands.
In contrast, voice assistants typically run on legacy hardware and rely on cloud-based processing for many of their features. Latency, stable internet connectivity, and cloud integration are all factors that limit their capacity for real-time responses.
Outdated hardware is another source of users' frustration. Many voice assistant devices have remained on the market with minimal hardware updates. While the software has evolved, the hardware has not kept pace, leading to a mismatch between the capabilities of the AI and the processing power of the device. In many cases, users must reset their devices frequently, as older hardware struggles to keep up with the demands of newer software updates.
Data privacy scrutiny in regions like the EU and the US has also contributed to the reduced functionality of smart home assistants. Companies are balancing convenience with stricter privacy laws, which, in turn, affects features like voice recognition and personalized responses. This trade-off makes devices less responsive and less capable of learning user habits, resulting in a more generic user experience.
This week’s New Tech Tuesday features product development tools from DFRobot and Arduino that can help design engineers build next-gen home assistants.
DFRobot’s LattePanda Mu Micro x86 compute module packs an Intel® N100 quad-core processor, 8GB of LPDDR5 RAM, and 64GB of storage. Connectivity options include three HDMI/DisplayPort outputs, up to eight USB 2.0 pins, up to four USB 3.2 pins, and nine PCIe 3.0 lanes. This module is ideal for designing handheld devices, AI robots, IoT projects, edge computing, voice recognition, and cloud machine learning.
The Arduino Portenta Environmental Monitoring Bundle is a versatile toolkit designed for developers, researchers, and hobbyists interested in building sophisticated environmental monitoring applications. The kit includes the powerful Arduino Portenta C33 system-on-module (SoM) and an Arduino Nicla Sense Env board with a temperature and humidity sensor, indoor air quality sensor, and outdoor air monitoring (NO₂, O₃) sensor. The Portenta Environmental Monitoring Bundle offers a combination of sensor integration, edge computing capabilities, wireless connectivity, and IoT compatibility that make it a powerful tool for real-time, location-based environmental data collection and analysis in applications such as smart cities, agriculture, indoor air quality monitoring, healthcare, and environmental research.
While AI is advancing in industries like healthcare, finance, and automation, the voice assistant market is facing challenges like technical debt, software fragmentation, cloud reliance, evolving natural language models, outdated hardware, and privacy concerns, all contributing to the user’s satisfaction decline, and questioning if voice assistants have reached their peak.
Sources:
[1] https://www.googlenestcommunity.com/t5/Speakers-and-Displays/Google-Home-assistant-is-getting-worse-and-worse/m-p/457868#M87618 [2] https://asana.com/resources/technical-debt
Rudy Ramos brings 35+ years of expertise in advanced electromechanical systems, robotics, pneumatics, vacuum systems, high voltage, semiconductor manufacturing, military hardware, and project management. Rudy has authored technical articles appearing in engineering websites and holds a BS in Technical Management and an MBA with a concentration in Project Management. Prior to Mouser, Rudy worked for National Semiconductor and Texas Instruments..