AI Leaders Index

The compute, models and silicon behind applied AI.

AILEADConviction weightedAggressiveRebalanced quarterly4 holdings

Sample data. Price history on this build is generated from a seeded model, not a live feed, so levels and returns are illustrative. Constituents, weights, index maths and fee logic are real.

Performance

Level

93.62

−6.38%

over 6M

Rebased to 100 on Jan 2, 2026

Risk and return

Volatility22.8%Annualised

Max drawdown-29.8%Peak to trough

Return / risk-0.57Excess over 4.2% cash

Blended yield0.25%Weighted dividend

Return over risk is computed against a 4.2% risk-free rate. Below zero means the index has not paid for the volatility it carried in this window.

Composition

4Holdings

Semiconductors53.0%
Software25.0%
Internet22.0%

ConcentratedTop weight 35.0% · behaves like 3.8 equal names

Constituents

NVDANVIDIA
+0.9pt35.0%
MSFTMicrosoft
+1.5pt25.0%
GOOGLAlphabet
−1.9pt22.0%
AMDAdvanced Micro Devices
−0.5pt18.0%

The rule

Conviction weighted

Weights are set by the index author rather than by a formula. Highest expression of a view, highest concentration risk.

Thesis

Exposure to the four companies that currently capture most of the economics of applied AI: the accelerator vendor, the two hyperscalers monetising models at scale, and the credible second source in GPUs. Weighted by conviction rather than market cap, because cap weighting here collapses into a single name.

Rebalance: Quarterly
Inception: Jan 2, 2026

What an allocation buys

Allocation$10,000.00

Fractional stock-token positions this allocation opens
Ticker	Weight	Price	Units	Value
NVDA	35.0%	$178.42	19.6166	$3,500.00
MSFT	25.0%	$512.18	4.8811	$2,500.00
GOOGL	22.0%	$246.70	8.9177	$2,200.00
AMD	18.0%	$164.85	10.9190	$1,800.00

Units are fractional by design — stock tokens are ERC-20s with 18 decimals, so weights resolve exactly at any allocation size rather than being rounded to whole shares.