Mala Gaonkar combats investment fads by replacing the "Fear of Missing Out" (FOMO) with "Thoughtfully Missing Out" (TOMO). This framework encourages her team to consciously and deliberately pass on hyped opportunities that fall outside their defined circle of competence, avoiding costly mistakes.
Mala Gaonkar's firm gains an advantage by deeply analyzing the technology infrastructure of companies in traditional sectors like aerospace or finance. This reveals scalability and quality often overlooked by investors focused solely on the core business, treating every company as a technology company.
Gaonkar's painful experience shorting Nokia, which was acquired by Microsoft despite its decline, taught her a key lesson. An investment thesis must account for a company's strategic value to others, not just its isolated performance. This requires systemic, not siloed, thinking.
Instead of relying on mainstream channels, Gaonkar believes change happens at the edges. She finds her most creative investment ideas by going "out in the field" to niche industry events and surveying developers on the ground, rather than just meeting with established leaders.
Significant disruption often comes from applying mature technologies in novel contexts, not just from new inventions. Gaonkar points to 1970s lithium-ion batteries revolutionizing EVs and old gaming GPUs now powering the AI boom as prime examples of this powerful investment thesis.
Gaonkar favors businesses with complex, "systemic" moats derived from deeply integrated processes, like TSMC's manufacturing expertise. She argues these are more durable than moats based on a single advantage, comparing it to owning the process of gold extraction rather than just owning the mine.
Mala Gaonkar's philanthropic work highlights a key limitation of AI: it excels at predicting "what" will happen but not "why." By integrating behavioral data, her organization aims to uncover the motivations behind human choices, enabling more effective interventions in areas like public health.
Gaonkar identifies her biggest error with NVIDIA wasn't selling too early, but failing to re-evaluate and buy back in later. The psychological pain of "sunk cost bias" makes it incredibly difficult to re-enter a position at a higher price, even when the fundamental thesis has improved.