Author presents an AI-heavy portfolio allocation plan with detailed rationales for semiconductors, cloud, healthcare, energy, financials, and defense/materials positions.
NVDA — LONG Author argues Nvidia commands the global GPU supply chain that is the fundamental unit for AI training and inference, and its chips are highly fungible across valuable uses. He dismisses depreciation concerns, citing A100s rented at 95% of original contract price after nearly six years, and expects the AI wave to continue expanding over 0-5 years. The main stated risk is depreciation, which he believes is overblown.
Key positions are Nvidia, which commands the global supply chain for GPUs, the fundamental unit for fueling AI's training and inference.
MU — LONG Author sees memory as fundamental to AI accelerators and the single largest cost in the bill-of-materials for GPUs, necessary regardless of ASIC share gains. He expects HBM memory suppliers to catch up to demand more slowly this cycle because supply expansion is constrained by advanced packaging, while memory consumption grows with hyperscaler capex. This supports the author's 6% allocation to MU.
Memory is fundamental to AI accelerators. They're the single largest cost in the bill-of-materials for GPUs and will continue to be necessary regardless of ASICs gaining market share.
GOOGL — LONG Author views Alphabet as the cloud leader with TPUs, Google Cloud, Gemini, and billions of daily app users. He argues cloud benefits because all inference demand funnels there, avoiding disruption risk and narrative swings; Alphabet stands out as the leader. The author allocates 12% to GOOGL.
Alphabet stands out as the leader here. They have TPUs, Google Cloud, Gemini, and a distribution base of apps with billions of daily users.
LLY — LONG Author calls Eli Lilly his healthcare champion, believing the pending oral GLP-1 will be revolutionary, cheap to produce, and high margin. He cites its massive obesity and diabetes market plus potential benefits in heart health, Alzheimer's, kidney health, sleep apnea, and inflammation, along with an Isomorphic Labs partnership for AI drug discovery. The product is pending approval.
Eli Lilly is my champion here. I believe the oral GLP-1 that is pending approval will be a truly revolutionary product. It can be produced cheaply and sold at a high margin.
MA — LONG Author says Mastercard is progressing in services to monetize data and agentic commerce. He believes agentic commerce could go parabolic in 1-2 years and MA owns the rails, supporting his 3% allocation. No specific risk is stated.
Mastercard is making headway in their services business to monetize data and agentic commerce. Agentic commerce has the potential to go parabolic in the coming 1-2 years and MA owns the rails.
JPM — LONG Author believes JPMorgan has significant potential to adopt AI to streamline operations such as algorithmic trading, loan assessment, research, and administrative tasks. He allocates 3% to JPM as part of the financial sleeve. No specific risk is stated.
JPM has a lot of potential to adopt AI to streamline much of their operations, including algorithmic trading, loan assessment, research, and all manner of administrative tasks.
SHLD — LONG Author uses defense ETF SHLD as a risk hedge against geopolitical conflict or a breakdown in the AI pipeline, and expects global rearmament and systems modernization to make it not dead weight. He allocates 6% to SHLD. The main stated risk scenario is geopolitical conflict or AI pipeline breakdown.
Global rearmament is a macro trend and systems are being modernized.
XLB — LONG Author holds materials ETF XLB as part of the defense and materials risk hedge, citing materials as a fundamental constraint especially copper for energization and data center interconnection. He allocates 4% to XLB. The risk hedge is for geopolitical conflict or AI pipeline breakdown.
Materials act as a fundamental constraint as well, especially copper for energization and interconnection in data center buildouts.
FSLR — LONG Author treats energy as a basket of power generation, grid, and transmission companies, including FSLR at 3%, because he sees a high probability that data center power demand exceeds available supply around 2028. He expects energy access to become the critical bottleneck for US AI expansion and these companies to be durable with accelerating growth over the coming decade. No specific risk is stated.
I'm treating these as a basket that represents power generation across sources, grid, and transmission infrastructure companies.