Expert Insights

Bobak Tavangar makes a compelling case for using AI more purposefully and responsibly in wearable tech. He underscores that his company, Brilliant Labs, bucked the trend by focusing on intelligence over graphics in the context of headworn devices - a notion initially deemed contrarian but retrospectively groundbreaking.

In an era where virtual reality and graphics-driven tech have dominated the development of smart glasses, he boldly argues for AI-driven, privacy-centric development. Hear Bobak explain:

    • Why Brilliant Labs is applying AI to wearable glasses to interpret the user’s real-world context, focusing on deeply personal, private experiences rather than simulated or mass-market ones.

    • How the company’s hardware philosophy differs from smartphone-replacement strategies, instead working with the phone to share compute, connectivity, and reduce unnecessary resource use.

    • The reasoning behind designing AI systems that process personal data locally and discard it immediately is that it ensures user information remains accessible only to the individual.

    • His view on how large language models and natural language programming are lowering the barrier to entry, enabling non-technical users to engage with code and participate in building products.

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We decided to differentiate by focusing on better intelligence, not better graphics. The glasses don't render new worlds. They try to understand the one you are actually in. As we moved forward in our journey, launching products that are super lightweight, fully multimodal, and entirely open source, we affirmed our belief in a form factor for giving an AI agent a front-row seat to life.quotation-marks icon

Monterail Team Analysis

Here are some actionable takeaways for software teams exploring the use of AI in wearable technologies and beyond:

  • Prioritize meaningful interaction over flashy design: When developing software for wearable devices like smart glasses, place the user's needs and desires at the core of the product. Rather than distracting with excessive graphics, aim to provide value through intelligent interactions and features.
  • Collaborate with existing technologies: Instead of challenging and trying to replace established technologies like smartphones, consider how your product can leverage and complement these devices to deliver a superior user experience.
  • Embed privacy at the core of your development: It's essential to create a trustworthy AI platform—one that performs AI inference on personal data and then deletes it. Subscribe to a privacy-by-design approach, ensuring you handle user data with the utmost responsibility, transparency, and care.
  • Engage non-technical users in development: With the advent of large AI language models and natural language programming, more users can now interact with your code, even without deep technical expertise. Foster an environment where user feedback and suggestions play a leading role in shaping the product.
  • Bring intelligence, not just automation: Develop software for devices to understand and relate to the world more intelligently, not merely automate tasks. The aim should be to create a private and custom user experience.
  • Measure the success differently: When working with AI, traditional KPIs may not be as relevant. Be ready to create new measures that account for user satisfaction, privacy safeguards, and AI performance, among other factors.