Show Notes
In this episode, Martin discusses how investors can evaluate buying opportunities during sharp market selloffs and determine when fear may have pushed prices below underlying value. He shares lessons on valuation, market psychology, position sizing, and managing risk when trading against panic, before analyzing Liquidia and examining its clinical prospects, competitive position, catalysts, valuation, and the risks shaping his investment outlook.
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In this episode, Martin explains why he believes increasing Chinese semiconductor supply could create significant pressure for Micron and the broader memory market. He outlines his bearish outlook on semiconductor stocks, examining supply growth, memory pricing, capital spending, and industry cycles, before discussing ASML and Micron (MU) and the risks and catalysts shaping his current short theses.
In this episode, Martin examines Cerebras and whether its AI chip architecture could challenge Nvidia’s dominance in accelerated computing. He also lays out his bullish thesis on MongoDB and discusses the factors that could support a $400 valuation target, before turning to Ginkgo Bioworks and explaining his bearish outlook on the company, including its business model, financial performance, valuation, and investment risks.
In this episode, Martin reflects on a major loss from his short position in Kodiak Sciences and examines how a short squeeze moved the trade against him. He discusses position sizing, risk management, conviction, and the lessons he draws from losing millions on the position before turning to Nvidia, where he explores ways investors can search for alpha by analyzing expectations, valuation, AI demand, and potential market inefficiencies.
In this episode, Martin reflects on a costly short position in Kodiak Sciences and examines what went wrong with the trade, including the risks of conviction, timing, and unexpected catalysts. He also discusses the possibility of OpenAI eventually going public and what such a move could mean for investors, before explaining why he believes many financial models fail to capture reality and how assumptions, probabilities, and changing business conditions can undermine even detailed forecasts.
In this episode, Martin examines Meta’s relationship with Scale AI and discusses how the company could use its investment and Scale AI’s valuation to strengthen its position in the artificial intelligence race. He also questions Intel’s characterization of demand and examines the underlying semiconductor-market dynamics before revisiting Moderna, discussing its pipeline, valuation, catalysts, and the risks shaping his current investment outlook.