How concentrated this is
Holdings5
Weighted like4.2 equal ones
LargestNVDA at 33.9%
Half the portfolio2 names
Exposed to the premise90%
Half of this book is 2 names. If they are wrong, the rest will not save it.
Compute is the binding constraint on artificial intelligence, not model design.
Universe v2 (assumed — this link predates the field)· No stated date on this link
Every holding, and why it is that size
Restore the portfolio33.9%Nvidia
Sits on the critical path of nearly every frontier training run.
NVDA · Equity · $218.97 · — · Compute buildout
−+×22.7%Arista Networks
The switching fabric between accelerators.
ANET · Equity · $192.93 · — · Compute buildout
−+×22.2%Semiconductor ETF
Broad exposure for the part that is not a single-name bet.
SMH · ETF · $574.29 · — · Compute buildout
−+×11.2%Broadcom
Custom accelerators for buyers who want off a single vendor's price list.
AVGO · Equity · $364.38 · — · Compute buildout
−+×10%0–3 Month Treasury Bill ETF
Cash that still earns while the thesis is early.
SGOV · Treasury · $100.49 · — · Ballast
The case for
Frontier training runs are gated by physical supply, not ideas. Leading-edge capacity is booked years out, high-bandwidth memory is sold before it is made, and grid interconnection queues run for years. Anything on that critical path collects the toll.
The case against
Every capex supercycle in semiconductors has ended in a glut. Efficiency gains, smaller models and custom silicon all cut the accelerators needed per unit of intelligence, and buyers are a handful of firms who can pause in one quarter.
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igitur.xyz/b/compute-is-the-binding-constraint-on-1whzpop