Sandbar, the startup behind the private voice ring Stream, is positioning itself at the forefront of the AI wearables revolution by emphasizing the power of voice-based technology. With over $36 million in funding, including a $23 million Series A round led by Adjacent and Kindred Ventures, Sandbar's co-founder and CEO Mina Fahmi argues that the key to successful wearable AI lies in giving users direct control over their digital interactions. This approach contrasts sharply with earlier attempts at voice-enabled wearables, which often failed due to poor user experience or lack of integration with daily life. The rise of AI-powered notetaking devices has gained momentum in recent years, with products ranging from credit-card-sized gadgets to earbuds capable of transcribing meetings into actionable insights. These tools aim to streamline communication and productivity, offering users a seamless way to capture fleeting thoughts and ideas. However, despite the growing demand, many such devices have struggled to gain widespread adoption, often due to limitations in usability, battery life, or contextual awareness. Fahmi attributes past failures to a misalignment between the technology and user expectations. She explains that early voice wearables were often clunky, requiring complex setup or failing to adapt to natural speech patterns. By contrast, Sandbar’s Stream ring is designed to be intuitive and unobtrusive, allowing users to interact with AI without disrupting their workflow. The device focuses on capturing spontaneous notes, reminders, and even creative ideation, making it a versatile tool for professionals and creatives alike. The company’s strategy hinges on maintaining the human element in AI interactions. Rather than relying solely on automation, Sandbar emphasizes user agency, enabling individuals to guide the AI’s responses and refine its output. This philosophy aligns with broader trends in AI development, where personalization and context-awareness are increasingly valued over rigid automation. As a result, Sandbar aims to create a wearable that feels like an extension of the user rather than a separate device. In parallel, another startup, Discovered Materials, is tackling a different but equally pressing issue in the tech sector: the overheating of AI-driven chips. Data centers, which rely heavily on AI processing, consume vast amounts of energy, much of it dedicated to cooling systems. To address this, Discovered Materials is leveraging AI to discover new materials that could enhance the efficiency of semiconductors while reducing heat generation. Founded by Advaith Sridhar and Akash Ramdas, the startup has secured $9 million in seed funding from Lightspeed India Partners, with additional support from Peak XV Partners and notable angel investors. Drawing on Ramdas' academic background in materials science from Stanford, the team has developed a software pipeline that utilizes Anthropic models to generate potential material candidates. These leads are then tested using foundational physics models to determine their viability. The process involves running thousands of simulations daily, significantly accelerating what was once a slow, manual task. During his doctoral studies, Ramdas could only make around 20 guesses per day, but now, with AI-driven agents working 24/7, the team can explore far more possibilities. They have already identified numerous promising materials that exhibit properties comparable to those used in current chip manufacturing, though specifics remain confidential. Despite these advancements, challenges persist. Engineering trade-offs often arise when evaluating new materials, what works in theory may prove impractical for mass production. As Hemant Mohapatra, a Lightspeed partner, noted, the real-world utility of a material depends on multiple factors converging simultaneously, making the discovery process akin to playing "whack-a-mole." Looking ahead, Discovered Materials plans to file patents for materials suitable for GPUs and other high-performance components, aiming to license these innovations to chip manufacturers. Sridhar anticipates that within the next year, the company will have several materials ready for commercial application. While AI has shown promise in drug discovery and material science, tangible commercial impacts remain limited. Notable exceptions include Insilico Medicine’s Renterosib, which advanced to Phase II trials, and ongoing efforts by companies like MatNex and Citrine Informatics. Yet, large-scale deployment of AI-discovered materials is still some time away.
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