Why AI hardware needs privacy built in from the start

Every new technology faces a common cycle. Companies push the boundaries of what a product can do, packing devices with numerous sensors and capabilities to differentiate themselves. Privacy concerns typically surface only after these products are released and customers provide feedback on their functionality and impact on daily life.

This pattern has been observed with social media, smartphones, and smart home devices, and it is starting to occur again as artificial intelligence becomes integrated into new generations of devices capable of sensing, interpreting, and interacting with the environment. I believe this approach is incorrect because privacy should not be an afterthought when a product is launched, as many of the decisions that affect its privacy aspects have already been made during its development.

Hardware choices, architecture, data collection and processing, and the amount of information a device needs to function should all be considered from the start, alongside other critical design decisions that determine the product's construction. Product teams are accustomed to considering battery life, weight, cost, and performance as design constraints from the outset.

Privacy must be treated in the same manner, especially as AI enables consumer devices to gather and analyze more data than ever before. Smart glasses exemplify this concept clearly. The reasoning behind using cameras in these devices is understandable: additional visual context enhances AI's capabilities, enabling the system to comprehend what the wearer sees and respond appropriately.

However, cameras also present one of the product's most significant trust issues since they are often used in front of individuals who did not consent to be observed. This situation creates a design consideration that is often overlooked when assessing a product solely from the wearer's perspective. While the wearer may be aware of the device's recording capabilities, the data processing, and the purpose of the sensor, those around the user have limited knowledge.

Product teams must also consider not only the desired experience for the end-user but also the broader implications, as once a device can capture information about people nearby, that capability can be utilized in ways the designers did not anticipate. A status light can indicate camera activation, and a privacy policy can explain how collected information is managed, but neither can completely eliminate the uncertainty introduced by the sensor's presence.

By this stage, the most critical privacy decision has already been made in hardware. Not every privacy issue can be resolved through better policy or software, particularly when a product relies on specific sensors, continuously transmits data to the cloud, or retains data by default, as privacy teams then face constraints set earlier in the development process.

The optimal starting question is not how much data a device can collect but how much data it truly needs to collect to achieve its primary purpose. This inquiry can lead product teams to make distinct decisions about what information a device must gather. Does an AI device require real-time identification of everything in the wearer's field of view, or does it merely need sufficient context to provide relevant information?

Does the data need to be transmitted to the cloud, or can some processing occur locally? Does the data need to be stored after each task, or can it be discarded once the task is complete? Does a sensor need to remain active continuously or only when the user explicitly requests it? These choices may limit certain capabilities in the short term, which is why privacy-by-design can be uncomfortable.

Product development typically rewards feature addition, while restraint may appear as choosing to do less. Consumer technology already necessitates product teams to make constant trade-offs, such as accepting smaller batteries for lighter devices, limiting processing to manage heat, or removing features that complicate the interface.

Privacy deserves the same level of discipline because the most capable version of a product is not always the version people will feel comfortable incorporating into their daily routines. Privacy is also a product experience. Discussions about privacy are frequently viewed as legal or compliance matters, but for consumer technology, they are also questions of user experience.

A device that technically complies with all requirements can still make users uneasy. This discomfort is particularly significant with technology designed for prolonged use or embedded in people's homes and surroundings. This is where I believe the next generation of AI hardware will be tested. As intelligence moves beyond phones and laptops, product teams will have access to increasingly advanced cameras, microphones, sensors, and models capable of interpreting the physical world in real-time.

The temptation will be to utilize all of these capabilities simply because they exist, when the more challenging design discipline is understanding which capabilities genuinely enhance the experience and when adding more creates a trade-off that is not worth making. For anyone developing in these categories, privacy should be addressed in the same conversations where teams determine which sensors are necessary, where computation occurs, what information is stored, and which features are essential enough to justify the trade-offs they introduce.

Waiting until launch to address these questions means many of the most crucial answers have already been predetermined. For wearable technology in particular, this discipline is essential.