Growth Strategy for AI-Powered Consumer Devices: (Part 1)
Over the years, the thrill of working in tech has always stemmed from witnessing the confluence of diverse technologies. It’s like conducting experiments in a chemistry lab, where one has the privilege of being the first to greet something new and extraordinary. The current wave of AI is now merging with versatile consumer devices, transforming them into intelligent tools for users. This article will explore the latest trend of AI-powered consumer devices, their unique growth dependencies, the various components driving their growth, and the pitfalls to avoid.
AI-Powered Consumer Devices
According to DARPAR’s John Launchbury, AI was said to come in three waves. The first wave came in the 1970s featuring rule-based procedures to apply step-by-step knowledge to a situation. The second wave saw the rise of machine learning and neural networks that made visual and voice recognition possible through algorithms. The third wave captures advances in deep learning and the ability of AI to interpret information and generate creative text formats, aiming for AGI to match human cognitive capacity.
With rapid development, AI continues to find new interfaces to interact with users. Hardware, especially consumer-facing devices, hosts various technologies and reflects the AI advancement and engineering marvel of its time.
Thanks to the breakthrough of Natural Language Processing (NLP) and voice recognition, we are now very familiar with smart speakers like Amazon Echo and Google Dots, and numerous IoT devices that perform a variety of tasks we assign them, such as safety monitoring, remote turning of light or even feeding pets.
Most recently, the confluence of Large Language Models (LLMs) & their applications and hardware, especially wearables, has become increasingly prominent — examples include Humane’s AI Pin, Meta’s Rayban Smart Glasses, Rabbit’s R1, and Brilliant Lab’s Frame. This wave of AI-powered hardware focuses on helping users get knowledge, information, and many more things in a faster and easier way. This is made possible thanks to a deep integration with multi-modal AI systems.
Spatial computing and XR devices benefit significantly from AI too. Today, AI empowers the infrastructures and computing of these devices. Tomorrow, it will do more on the application layer to expand the use cases of XR — for example, content creation, creative expression, enhanced image recognition, object detection, real-time scene analysis, conversational human-device interactions, and understanding user intent and cognitive load.
Unique Growth Dependencies
Many aspects of the growth strategy apply to AI-powered hardware. However, before we delve into the details, I want to emphasize the crucial differences that need to be acknowledged when developing the product marketing or GTM strategy for such products.
Broad challenges
In contrast to software development, hardware development has unique and multifaceted challenges to address, such as managing substantial upfront capital investments, optimizing form factors, streamlining supply chains, achieving yield rate targets, integrating software components, and establishing online and offline channels for product demo, trial, and distribution.
At the functional level, the research and development of a specific technology can act as a bottleneck for product development, such as batteries. A wearable product marketed for productivity enhancements would fall short of expectations with a battery life of only two hours. Additionally, the computing resources required by AI will only exacerbate this issue. These factors have a significant impact on the initial success, growth, and even the survival of a hardware product.
From a software partnership perspective, carefully selecting the hardware on which to place the product requires an understanding of factors such as the target audience for the device’s form factor, its size, the level of developer support available, and the opportunity cost involved.
Long lead-time
Devices can get over-the-air (OTA) software updates, enhancing and adding new features. But unlike software development, where rapid iteration is preferred, hardware can’t easily change its form factor, exceed capacity limits, or defy the law of physics. Hence, formulating a growth strategy for consumer devices involves early decisions and increased risks following trade-offs.
Moreover, consumer devices often require long lead-time due to the necessity of physical production and supply chain logistics. This means that companies must anticipate future trends and demands well in advance, as any changes to the design or specifications of a device can significantly impact its production timeline. This extended lead-time adds complexity and uncertainty to the product development process, as companies must balance the need for innovation with the practical limitations in the physical world.
Dependencies
Consumer devices rely on a complex web of dependencies, with hardware development closely tied to the advances of many innovations like chip design, accelerators, sensors, and optics. It is impossible for any one company, not even Apple, to use everything made by itself. Therefore, partnership plays an important role in developing better products, faster time to market, and growing a larger user base.
For example, Apple’s iPhone relies on components from a variety of suppliers, including Samsung for displays, and Sony for camera sensors. Such dependencies can make it difficult for companies, smaller ones in particular, to innovate quickly, as they are often reliant on the progress of other companies.