Leveraging LLMs to uncover the shifts transforming markets (afridi.io)

An experiment used custom scripts and large language models to transcribe and analyze the most recent ~160 episodes (200+ hours) from four investing podcasts—Invest Like The Best, The Twenty Minute VC, The Acquirer’s Podcast, and Dry Powder—surfacing over 1,200 distinct “shifts” and categorizing them via a PEST (Political, Economic, Social, Technological) framework. By querying LLMs against the episode transcripts, the author performed a breadth-first, automated synthesis of investor and operator conversations to highlight the themes dominating market mindshare without manually listening to hundreds of hours of audio. The outputs crystallize a set of cross-industry mega-shifts—AI’s pervasive impact, an infrastructure renaissance (energy/electrification), geopolitical reshoring and resilience, renewed interest in defense, climate adaptation, aging-population effects, workforce disruption from hybrid work and automation, B2B consumerization, creator-economy maturation, physical autonomous systems, rapid digitization in emerging markets, expanding private markets, asset-class convergence, and increasing government/enterprise crypto adoption. Methodologically, the pipeline relied on transcript generation, LLM querying, and prompt/filtering heuristics; the author notes limitations (noise, temporal bias, and room for prompt engineering) but demonstrates that LLMs can efficiently turn noisy audio into tractable, strategic market intelligence for investors, researchers, and builders.