In the modern investment landscape—where artificial intelligence has become one of the most alluring yet ambiguous frontiers—distinguishing genuine technological innovation from overhyped promises has evolved into an essential skill. Leonis Capital has developed a meticulous, research-driven framework to discern whether an AI startup’s core technology truly delivers on its claims. Rather than being dazzled by sleek presentations or buzzwords such as “deep learning,” “generative,” or “autonomous,” their investors—Jenny Xiao and Jay Zhao—focus on measurable evidence of performance, scalability, and impact.
Their approach begins with a fundamental inquiry: does the startup possess a proprietary algorithm or unique implementation architecture that meaningfully improves upon existing methods? Many founders reference cutting-edge models, but only a fraction can demonstrate empirical validation—through data accuracy benchmarks, latency reduction, or cost efficiencies observed in rigorous testing environments. Leonis Capital insists on transparency regarding the methodology behind these claims. They often ask startups to provide reproducible experimentation results, ideally corroborated by peer-reviewed evidence, pilot deployments, or customer outcomes that extend beyond idealized lab conditions.
Furthermore, Xiao and Zhao emphasize assessing the strength of the founding technical team. Investors look for a group that not only understands advanced AI principles but can also translate theoretical models into sustainable, real-world solutions. For instance, an impressive machine learning engineer is valuable, but a cohesive team capable of refining feedback loops, integrating new data streams, and continuously optimizing predictive performance distinguishes long-term innovators from short-lived ventures.
Beyond technical scrutiny, the firm deeply examines market fit and defensibility. A powerful algorithm, however sophisticated, becomes irrelevant if its application lacks commercial viability or user adoption potential. Leonis Capital therefore challenges founders to articulate the specific problem their AI solves, who benefits from it, and how it sustains competitive differentiation once competitors inevitably replicate similar technology. They value startups that design with a systems perspective—anticipating regulatory shifts, data privacy obligations, and operational complexities—thus building resilience from inception.
Through this holistic evaluation, Leonis Capital demonstrates that responsible AI investing requires both intellectual rigor and industry empathy. They remind entrepreneurs and investors alike that genuine innovation is not merely about developing machine intelligence—it is about grounding such intelligence in ethical design, demonstrable utility, and long-term adaptability. The outcome of their process is not simply financial due diligence; it is a philosophical commitment to fostering technologies that transform rather than merely imitate progress. In an era saturated with hype, Leonis Capital’s method offers a rare model of clarity and conviction for those striving to distinguish true AI breakthroughs from transient smoke and mirrors.
Sourse: https://www.businessinsider.com/leonis-capital-investor-questions-for-ai-startups-2026-1
