Laurie Goodman

"60% of [NVIDIA's] revenue is still coming from the hyperscalers" (Ed Ludlow). "We have a full allocation to the tech stocks... the big hyperscalers" (Tony Roth). Public software companies are "presumed to be higher quality" and less susceptible to disruption than private ones (Laurie Goodman). The AI infrastructure buildout demand is still skyrocketing and constrained by compute supply. The primary customers and beneficiaries are the large, public cloud hyperscalers (Microsoft Azure, Google Cloud, Meta's AI, Amazon AWS). They have the capital, in-house talent, and scale to both drive and withstand AI disruption, unlike smaller, over-leveraged private software companies. They are the conduit for AI adoption. LONG on the dominant public hyperscalers. They are the capital goods providers for the AI cycle, have locked-in demand from NVIDIA's guidance, and are insulated from the private credit markdown crisis affecting their smaller competitors. Geopolitical disruption to tech supply chains (e.g., helium, sulfuric acid from the Middle East); eventual competition from in-house AI chips reducing reliance on NVIDIA; regulatory scrutiny.

"The issue is really focused on Direct Lending... There has been a really big buildup in the assets in that space... Software is more than 20% of private credit, direct lending broadly... AI is challenging that narrative as we speak." Large private credit and Business Development Company (BDC) managers, like Blackstone (BX) and Ares Capital (ARCC), have significant exposure to direct lending portfolios heavily weighted towards software companies. These loans were made at high valuations based on growth assumptions that AI is now disrupting. This will lead to loan impairments, markdowns, and investor redemptions, creating a liquidity crisis specifically for these asset managers. SHORT the leading publicly-traded private credit managers and BDCs most exposed to direct lending and software. They face a cycle of writedowns, reduced fee income, and fund outflows. A swift resolution to the software/AI disruption narrative; strong underlying cash flows from portfolio companies; managers successfully re-categorize or restructure assets to avoid marks.

"The issue is really focused on Direct Lending... There has been a really big buildup in the assets in that space... Software is more than 20% of private credit, direct lending broadly... AI is challenging that narrative as we speak." Large private credit and Business Development Company (BDC) managers, like Blackstone (BX) and Ares Capital (ARCC), have significant exposure to direct lending portfolios heavily weighted towards software companies. These loans were made at high valuations based on growth assumptions that AI is now disrupting. This will lead to loan impairments, markdowns, and investor redemptions, creating a liquidity crisis specifically for these asset managers. SHORT the leading publicly-traded private credit managers and BDCs most exposed to direct lending and software. They face a cycle of writedowns, reduced fee income, and fund outflows. A swift resolution to the software/AI disruption narrative; strong underlying cash flows from portfolio companies; managers successfully re-categorize or restructure assets to avoid marks.

"60% of [NVIDIA's] revenue is still coming from the hyperscalers" (Ed Ludlow). "We have a full allocation to the tech stocks... the big hyperscalers" (Tony Roth). Public software companies are "presumed to be higher quality" and less susceptible to disruption than private ones (Laurie Goodman). The AI infrastructure buildout demand is still skyrocketing and constrained by compute supply. The primary customers and beneficiaries are the large, public cloud hyperscalers (Microsoft Azure, Google Cloud, Meta's AI, Amazon AWS). They have the capital, in-house talent, and scale to both drive and withstand AI disruption, unlike smaller, over-leveraged private software companies. They are the conduit for AI adoption. LONG on the dominant public hyperscalers. They are the capital goods providers for the AI cycle, have locked-in demand from NVIDIA's guidance, and are insulated from the private credit markdown crisis affecting their smaller competitors. Geopolitical disruption to tech supply chains (e.g., helium, sulfuric acid from the Middle East); eventual competition from in-house AI chips reducing reliance on NVIDIA; regulatory scrutiny.

"60% of [NVIDIA's] revenue is still coming from the hyperscalers" (Ed Ludlow). "We have a full allocation to the tech stocks... the big hyperscalers" (Tony Roth). Public software companies are "presumed to be higher quality" and less susceptible to disruption than private ones (Laurie Goodman). The AI infrastructure buildout demand is still skyrocketing and constrained by compute supply. The primary customers and beneficiaries are the large, public cloud hyperscalers (Microsoft Azure, Google Cloud, Meta's AI, Amazon AWS). They have the capital, in-house talent, and scale to both drive and withstand AI disruption, unlike smaller, over-leveraged private software companies. They are the conduit for AI adoption. LONG on the dominant public hyperscalers. They are the capital goods providers for the AI cycle, have locked-in demand from NVIDIA's guidance, and are insulated from the private credit markdown crisis affecting their smaller competitors. Geopolitical disruption to tech supply chains (e.g., helium, sulfuric acid from the Middle East); eventual competition from in-house AI chips reducing reliance on NVIDIA; regulatory scrutiny.

"60% of [NVIDIA's] revenue is still coming from the hyperscalers" (Ed Ludlow). "We have a full allocation to the tech stocks... the big hyperscalers" (Tony Roth). Public software companies are "presumed to be higher quality" and less susceptible to disruption than private ones (Laurie Goodman). The AI infrastructure buildout demand is still skyrocketing and constrained by compute supply. The primary customers and beneficiaries are the large, public cloud hyperscalers (Microsoft Azure, Google Cloud, Meta's AI, Amazon AWS). They have the capital, in-house talent, and scale to both drive and withstand AI disruption, unlike smaller, over-leveraged private software companies. They are the conduit for AI adoption. LONG on the dominant public hyperscalers. They are the capital goods providers for the AI cycle, have locked-in demand from NVIDIA's guidance, and are insulated from the private credit markdown crisis affecting their smaller competitors. Geopolitical disruption to tech supply chains (e.g., helium, sulfuric acid from the Middle East); eventual competition from in-house AI chips reducing reliance on NVIDIA; regulatory scrutiny.