AI has turned memory—from DRAM and HBM to NAND and enterprise storage—into the central bottleneck of computing. As that happens, memory-focused ETFs have gone from niche to headline, with assets under management (AUM) exploding in a short span of time. Along the way, another, quieter variable starts to matter: tracking deviation. How closely do these funds actually follow the memory indices they promise to track? And how does that tracking behave as AUM grows from tiny to massive?

This post explores the idea that the relationship between AUM and tracking deviation for memory ETFs is not a simple straight line. It is nonlinear, shaped by liquidity, portfolio construction, flows, and the peculiar cycles of memory pricing. We will keep the framework flexible and polished, moving between intuition and more technical reasoning, because tracking deviation lives at the intersection of theory and market practice.

What Do We Mean by Tracking Deviation?

Tracking deviation (or tracking error, depending on the definition used) is the difference between the ETF’s performance and the performance of the index it’s meant to follow. In simple form, it is often calculated as the absolute difference between daily ETF NAV returns and index returns, aggregated over time.

For memory-themed ETFs, tracking deviation reflects several underlying factors:

• The liquidity cost of trading memory stocks in the underlying basket.
• Management fees and other structural expenses.
• Cash holdings and the mechanics of creation/redemption.
• Rebalancing decisions and imperfect replication of index weights.

AUM, on the surface, is just a measure of size. But size changes how these factors interact, especially in sectors where some constituents are highly liquid mega-caps and others are thinly traded small caps tied to specialized memory niches.

Common Intuition: “Bigger AUM = Better Tracking”

In many ETF discussions, there is a simple rule of thumb: larger funds tend to have better tracking. The reasons are intuitive:

• More assets can support more efficient market-making, tighter spreads, and better arbitrage between ETF and underlying.
• Higher trading volume improves secondary liquidity, making it easier for authorized participants to handle creations and redemptions.
• Scale can lower per-unit operational costs, indirectly helping tracking by reducing friction.

For broad, diversified equity ETFs, there is empirical support for a negative relationship between fund size and tracking error—larger funds often track better, all else equal. Memory ETFs, however, inhabit a more specialized space. The underlying basket is more concentrated and more cyclical. AUM doesn’t just scale; it changes how the fund interacts with its underlying market. That is where nonlinearity enters.

Phase One: Small AUM, High Friction

At launch, memory ETFs like DRAM or similar products start with small AUM—tens of millions, maybe a bit more. In this early phase, tracking deviation tends to be relatively high:

• Secondary liquidity is limited; spreads can be wide compared to mature ETFs.
• Authorized participants may be cautious, leading to less aggressive arbitrage and more “drift” between ETF and index prices.
• Underlying memory stocks, especially smaller names, may be expensive to trade relative to the ETF’s size, leading to imperfect replication and rebalancing.

In this phase, incremental AUM often improves tracking. As funds cross certain thresholds, more market makers engage, spreads tighten, and tracking deviation falls. The relationship between AUM and tracking deviation is roughly negative—more assets, less deviation—in line with common intuition.

Phase Two: Mid-Sized AUM, Sweet Spot of Tracking

As memory ETFs grow into mid-sized territory—hundreds of millions in assets—the tracking characteristics often improve further and stabilize:

• The fund has enough scale to trade efficiently across its basket of memory stocks, including both large, liquid names and mid caps.
• Market makers and high-frequency designated sponsors can provide tighter spreads and more effective price alignment, reducing liquidity costs.
• Creation/redemption flows become more regular, making replication of the index smoother, especially in physically replicated funds.

In this intermediate zone, each additional dollar of AUM may have diminishing marginal benefit for tracking. The relationship between AUM and tracking deviation starts to flatten: tracking is relatively tight and changes only slowly as the fund grows. This is the “sweet spot” many investors prefer, where the ETF is large enough to be efficient but not so large that it distorts its own market.

Phase Three: Very Large AUM, Emerging Nonlinear Effects

Once a memory ETF becomes very large—approaching or exceeding several billion in AUM, as some AI memory funds have begun to do—the relationship can turn nonlinear in the other direction. Tracking deviation may start to rise again or become more erratic:

• Liquidity strain on small and mid caps: Large rebalancing trades and creations/redemptions can represent significant fractions of daily volume in thinner memory stocks, raising impact costs and slippage.
• Crowding and cyclicality: When the ETF itself becomes a major player in a cyclical memory universe, its flows can amplify price swings and reduce the ease of matching index weights, especially around earnings or cycle turns.
• Nonlinear liquidity relationships: Studies of ETF tracking error show that liquidity and expense-ratio effects on tracking can become nonlinear, with concave or convex shapes beyond certain thresholds.

In this high-AUM regime, an additional dollar of assets can worsen tracking deviation rather than improve it, particularly if the fund continues to own illiquid small cap memory names. The relationship between AUM and tracking deviation is no longer monotone; it resembles a curve with a “U” or “flatten-then-rise” shape.

Key Drivers of Nonlinearity in Memory ETFs

Several specific factors give memory ETFs their nonlinear AUM–tracking profile:

• Concentration in a few large names: Most memory ETFs allocate heavily to a small cluster of giants. As AUM grows, these positions can be managed efficiently, but index rules may push more weight into smaller names over time, increasing the marginal liquidity cost and tracking deviation.
• Cyclical volume changes: Memory trading volumes spike during hot AI cycles and drop in quieter periods. A large ETF that grows during hot phases may face higher tracking deviation when volumes normalize, even as AUM stays high.
• Cash and rebalancing mechanics: High flows can lead funds to hold more cash between trades or adjust their rebalancing cadence, introducing small but significant deviations from index behavior.
• Distribution patterns: How and when the ETF distributes cash relative to NAV has been shown to affect tracking error nonlinearly in other contexts; similar dynamics can appear in memory-themed products.

Put simply, memory ETFs interact with their markets differently at different scales. The underlying liquidity structure is not linear, so neither is the AUM–tracking relationship.

Illustrative Shape: A Nonlinear Curve

If we sketched the relationship between AUM (on the horizontal axis) and tracking deviation (on the vertical axis) for a memory ETF, it might look like:

• Small AUM: Higher tracking deviation—ETF is young, spreads are wide, replication imperfect.
• Medium AUM: Lower tracking deviation—liquidity improves, market makers engaged, rebalancing efficient.
• Very large AUM: Tracking deviation stabilizes or rises—liquidity strain on small caps, cyclicality, and non-linear liquidity costs start to matter.

This shape echoes findings in broader ETF research, where liquidity costs and expense ratios have concave or convex relationships with tracking error rather than simple linear ones. The memory segment adds its own twist by concentrating impact in a few cyclical sub-industries.

Implications for ETF Designers

For those designing AI storage and computing power ETFs with memory exposure, the nonlinear AUM–tracking relationship is not just an academic curiosity—it informs practical choices:

• Index construction: How many small and mid cap memory names are included, and at what weights, affects how tracking behaves as AUM scales. Liquidity-aware weighting can reduce nonlinearity at high AUM.
• Rebalancing strategy: Staggered rebalancing or utilization of liquidity windows around high-volume days can smooth tracking deviation for large funds.
• Use of derivatives: Incorporating index futures or swaps for part of the exposure, particularly in thinner segments, can help manage tracking when direct trading in small caps becomes costly.

ETF designers should think of AUM not as a one-dimensional target (“bigger is always better”), but as an input into how the fund will behave in its specific sector—especially one as structurally cyclical and concentrated as memory.

Implications for Investors: When Size Helps and When It Hurts

Investors evaluating memory ETFs should be aware that size is a nuanced signal:

• Too small: Very small memory ETFs may experience wide spreads and higher tracking deviation, particularly in their early months.
• Comfort zone: Medium-sized funds often offer a balance of thematic purity and reasonable tracking, with enough depth to support efficient trading but limited market distortion.
• Too large (for the niche): Extremely large memory ETFs in a relatively narrow segment can begin to show creeping tracking deviation, especially when flows are strong and underlying small caps are thinly traded.

For long-term thematic exposure, investors might prefer funds that sit in the “comfort zone” of AUM, or at least understand the trade-offs involved in holding very large products. For short-term trading, awareness of tracking dynamics across AUM regimes can help in timing entries and exits.

Cross-Index and Derivative Interactions

The nonlinear AUM–tracking relationship also affects how memory ETFs interact with other AI infrastructure indices and derivatives:

• Overlay strategies: When using memory ETFs alongside broader AI infrastructure or compute ETFs, understanding tracking behavior at current AUM levels helps calibrate overlays and hedges.
• Options and futures pricing: If tracking deviation becomes more volatile at high AUM, options on the ETF may reflect higher implied volatility or wider pricing ranges, impacting hedging and directional trades.
• Relative value trades: Traders comparing memory ETFs to broader semiconductor benchmarks must consider that tracking deviation itself may be part of the performance differences, especially during periods of rapid asset growth.

In this sense, tracking deviation is not just a “background statistic”—it becomes another variable in how AI storage and computing power themes are expressed in portfolios and trades.

Nonlinearity as a Reminder: Themes Have Plumbing

The nonlinear relationship between AUM and tracking deviation for memory ETFs is a reminder that thematic investing, especially in fast-moving areas like AI storage and computing power, has plumbing. Beneath the exciting thesis about HBM and DRAM shortages lies a set of practical constraints: liquidity costs, rebalancing mechanics, creation/redemption processes, and the subtle feedback loops between ETF flows and underlying markets.

A flexible view accepts that:

• AUM can improve tracking up to a point, but beyond that point, it can introduce new frictions.
• Memory’s cyclical nature amplifies these effects, because liquidity and volatility ebb and flow with pricing cycles.
• Thoughtful design and transparent communication can help investors navigate these nonlinearities rather than being surprised by them.

In the end, understanding the curve—how tracking deviation evolves as memory ETFs grow—is part of understanding the theme itself. AI does not run only on compute; it runs on memory. And memory-themed ETFs do not run only on narratives; they run on the structure of markets. Seeing both halves of that equation is what turns a catchy theme into a well-managed exposure.