The central investment strategy is to identify and invest in a select few businesses with sustainable, long-term competitive advantages, or "moats." This is defined by high incremental returns on invested capital (ROIC) driven by multiple levers, such as process innovation and market growth, not just pricing power.
Gaonkar posits that nearly every modern business is a technology business at its core. Her team focuses on how technology, including older innovations, intersects with and disrupts traditional industries like healthcare, automotive, and aerospace.
The discussion on AI distinguishes between the "spiky," project-based nature of model training and the recurring, "annuity-like" revenue stream of inference (the day-to-day use of models). Gaonkar sees durable value in companies enabling cost-effective inference, such as those developing Application-Specific Integrated Circuits (ASICs).
The conversation highlights the transformative, yet often underappreciated, impact of AI in healthcare. Specific examples include AI improving the speed and accuracy of medical imaging (MRIs, CT scans) by up to 70% and enhancing the precision and accessibility of the 300-million-procedure global surgery market through robotics.
Gaonkar emphasizes the importance of a disciplined investment process designed to mitigate human cognitive biases. This includes using data science for objective checks, maintaining a small and collaborative team for creative thinking, and having a methodology to avoid emotional decision-making.
Keep pulling the thread on Mala Gaonkar.