Author argues the AI build-out's best risk/reward is in infrastructure 'picks and shovels' — HBM memory (SK Hynix, Samsung), semiconductor equipment (ASML, KLA, Teradyne) and power/cooling/real estate — rather than GPU designers like Nvidia, and offers a 20-page report via DM.
000660.KS — LONG The author argues the real AI winners are infrastructure 'picks and shovels', and that SK Hynix is one of the HBM bottlenecks in the memory supply chain. The stated mechanism is that $0.30-$0.50 of every dollar spent on a GPU goes to HBM, a sub-sector growing faster than GPUs, funded by record hyperscaler capex planned for 2026. The author claims these infrastructure plays are less volatile and less 'priced in' than Nvidia.
High Bandwidth Memory (HBM): SK Hynix and Samsung are the bottleneck. For every $1 spent on a GPU, $0.30-$0.50 is spent on HBM. This sub-sector is growing even faster than GPUs.
005930.KS — LONG The author argues Samsung is one of the two HBM supply bottlenecks positioned to capture a large share of memory spending in the AI build-out. The mechanism cited is that $0.30-$0.50 of every GPU dollar is spent on HBM, a faster-growing sub-sector, driven by hundreds of billions of dollars of hyperscaler capex planned for 2026. The author frames such infrastructure names as less volatile and less 'priced in' than GPU designers.
High Bandwidth Memory (HBM): SK Hynix and Samsung are the bottleneck. For every $1 spent on a GPU, $0.30-$0.50 is spent on HBM. This sub-sector is growing even faster than GPUs.
ASML — LONG The author argues ASML is a gatekeeper of the semiconductor equipment layer and an indispensable beneficiary as chip designs become exponentially more complex. The stated mechanism is that hyperscaler capex is flowing not just to GPUs but to specialised equipment, where testing and verification costs are exploding. The author claims these equipment names offer sustained growth with less volatility and are less 'priced in' than Nvidia.
Semiconductor Equipment: Companies like ASML, KLA, and Teradyne are the gatekeepers. As chip designs get exponentially more complex, the cost of testing and verification is exploding. These companies are indispensable.
KLAC — LONG The author identifies KLA as a semiconductor equipment 'gatekeeper' that is indispensable as chip complexity drives exploding testing and verification costs. The mechanism is that AI data-centre capex extends beyond GPUs into specialised equipment, so these suppliers benefit from the whole build-out rather than only GPU demand. The author contends these infrastructure plays are less volatile and less 'priced in' than the headline chip designers.
Semiconductor Equipment: Companies like ASML, KLA, and Teradyne are the gatekeepers. As chip designs get exponentially more complex, the cost of testing and verification is exploding. These companies are indispensable.
TER — LONG The author names Teradyne as one of the semiconductor equipment gatekeepers positioned to benefit from rising test and verification spending as chip designs grow more complex. The mechanism is that hyperscaler AI capex flows into specialised equipment, not only GPUs, making these suppliers indispensable to the ecosystem. The author argues such infrastructure plays offer sustained, less volatile growth and are less 'priced in' than Nvidia.
Semiconductor Equipment: Companies like ASML, KLA, and Teradyne are the gatekeepers. As chip designs get exponentially more complex, the cost of testing and verification is exploding. These companies are indispensable.