Joining the conversation is Lu Zhang, founder and managing partner of Fusion Fund, who shared insights on the rapidly evolving artificial intelligence landscape and its implications across multiple industries, particularly healthcare. Zhang’s perspective offers clarity in an environment often described as crowded with AI noise, where discerning meaningful innovation has become increasingly critical for investors and operators alike.
Over the past decade, Zhang has focused extensively on artificial intelligence and believes the next 12 to 18 months will center primarily on AI infrastructure. She argues that competitive advantage is shifting away from simply developing the most advanced AI models toward building cost-efficient systems that significantly reduce GPU usage and energy consumption. Zhang emphasized the growing importance of deploying AI on edge devices, a transition that opens new possibilities across industries as companies look to embed AI directly into their operations. This evolution presents a substantial opportunity for both innovation and targeted investment.
Healthcare remains a major area of focus for Zhang, particularly due to its abundance of high-quality data that remains largely underutilized. She noted that approximately 30% of all data generated by human society is related to healthcare, yet only about 5% of that data is currently being used effectively. Data fragmentation continues to limit progress, but Zhang believes federal computing infrastructure could help consolidate healthcare data, enabling more advanced AI applications. These range from digital diagnostics to personalized treatment strategies for chronic conditions such as cancer, heart disease, and mental health disorders.
Zhang expressed particular passion for the application of AI in mental health, citing its growing urgency in modern society. More than 20% of the U.S. population experiences depression, with rates continuing to rise among younger demographics. Mental health conditions are highly individualized, making standardized treatments less effective. Zhang outlined Fusion Fund’s investments in emerging technologies aimed at addressing these challenges, including advances in microglia cell research and high-density ultrasound therapies. These approaches could offer alternatives to traditional pharmaceutical treatments and introduce new pathways for patient care.
As an experienced founder herself, Zhang brings a distinct lens to evaluating startup teams. She outlined several qualities she believes are essential for success, including a clear long-term vision, unique insight into the problem being solved, and the ability to attract and retain top talent. In an environment defined by rapid change, she stressed that adaptability is critical, as founders must make decisive moves under uncertainty. Resilience, she added, remains one of the most important traits throughout the entrepreneurial journey.
When assessing AI startups, particularly those targeting enterprise applications, Zhang highlighted data access as a decisive factor. Founders must demonstrate the ability to curate high-quality data sets and continuously refine their models to maintain an edge. As AI adoption expands across finance and healthcare, demand is growing for solutions that can unlock value from vast pools of underused data.
Zhang also addressed the rising costs associated with building and scaling AI companies. She categorized startups into two primary groups: those developing foundational models that require substantial capital and those building vertical, application-specific models. Companies pursuing smaller, specialized models can often operate more efficiently by keeping GPU and energy costs lower. This strategy reduces financial strain while allowing for meaningful revenue growth with comparatively modest investment.
Overall, Lu Zhang’s insights highlight the shifting priorities within AI investment and deployment, particularly in healthcare. As artificial intelligence continues to reshape industries and tackle complex global challenges, both entrepreneurs and investors must remain agile and focused on practical impact. Zhang’s perspective underscores how infrastructure efficiency, data accessibility, and targeted innovation will define AI’s next chapter, with the potential to deliver lasting solutions in areas such as mental health and broader societal well-being.