AI Token Futures and Cost Per Token: It's Now a Cost Race, Not a Performance Race

📚 Context for follow-up posts This is a follow-up to Dell’s Earnings Surprise and Korean Semiconductors and Marvell Q1 FY2027 and Korean Semiconductors. Where the first two posts argued that “the AI bottleneck migrates from the GPU down to memory, interconnect, substrate and power — and who earns the margin at that bottleneck,” this post goes one step further. Once AI usage itself gets a market price, the battleground shifts from “a faster chip” to “a cheaper token.” Related hubs: AI HBM Hub, Korea Semiconductor Value Chain Hub, AI PCB & Substrate Hub.

TL;DR

Until now, AI investing has been a fight over “who builds the more powerful chip.” But a quiet signal has just switched on. AI usage itself is starting to get a market price.

According to Reuters, China’s Shanghai Futures Exchange (SHFE) is early-designing futures linked to AI token prices. In the U.S., CME Group (with Silicon Data) and ICE (with Ornn) announced plans for GPU compute futures. None of these are listed, confirmed products yet. But the direction is clear.

Once AI compute and tokens have a “market price,” the industry’s axis shifts from a performance race to a cost race.

It is the same as when oil got a futures price: refining, shipping and power generation all lined up by “cost per barrel.” Once AI’s price per token becomes visible, every participant asks the same question: “What does it cost me to produce one token?"

The answer is not generic “AI stocks.” It is the bottleneck parts, chips and platforms that actually lower the customer’s cost per token. The priority order:

The Korea bottom line first: Samsung Electronics is the most balanced practical pick. HBM4E catch-up + DDR5 + SOCAMM2 + PCIe Gen6 eSSD + KV-cache storage give it the broadest exposure across the token-cost stack. SK Hynix is the best-quality name but crowded. Samsung Electro-Mechanics has high thesis purity but price matters. Daeduck Electronics is a second-order FC-BGA beta candidate.

1. What Is Happening: The Financialization of AI Usage

First, separate the facts.

• [Fact] Per Reuters, SHFE is early-designing futures linked to AI token prices. The contract specification, the definition of the underlying (which token), the settlement method, and the approval/launch timeline are all unconfirmed.
• [Fact] CME Group announced plans to launch compute futures with Silicon Data; ICE announced plans for GPU compute futures contracts with Ornn.

What matters here is not “futures are already listed.” They are not. What matters is that the industry has started trying to put a price on AI usage.

An analogy: imagine using electricity without any standard price of “X per kilowatt-hour.” The moment a power exchange opens and prices get quoted, every factory starts computing “how much electricity do we use per unit of product?” AI is now standing at exactly that threshold.

When the price becomes visible, the cost becomes visible. When the cost becomes visible, whoever lowers it wins.

2. Why This Changes the Rules: Performance Race → Cost Race

For years the AI race was about “benchmark scores” — who runs a bigger model on a faster chip. Investing followed the same logic: “buy the company that makes the most powerful GPU.”

Once usage gets a price, the question changes.

Cost per token, in the simplest form:

Cost per token = total compute cost / tokens processed

You lower it by shrinking the numerator (compute cost) or growing the denominator (tokens processed). Whoever extracts more tokens from the same electricity and the same capital wins. So two metrics become the new battleground:

Tokens per watt = tokens/sec / power (W)
Tokens per dollar = tokens / total cost ($)

Per Reuters, TSMC sees chip-design focus shifting from raw performance to power efficiency because of AI power demand — meaning tokens per watt becomes a key metric.

In investment language, this is clear:

Not “buy AI stocks,” but “buy the companies that lower the customer’s cost per token."

3. The Kitchen Analogy: The Expensive Chef Is Sitting Idle

Let’s make it more concrete.

A GPU is a very expensive chef. Their hands are fast, but they can only cook if the ingredients arrive on time. The conveyor belt that brings the ingredients is the memory (HBM).

What happens if the belt is slow? The expensive chef stands around waiting for ingredients. The wage clock keeps running. No tokens come out. Cost per token spikes.

So the real bottleneck is often not “hire another chef” (= a more powerful GPU). It is more effective to speed up the belt and pre-stack the frequently used ingredients next to the chef (= memory bandwidth + cache).

KV-cache storage is exactly the act of “pre-stacking frequently used ingredients nearby.” It avoids redoing the same calculation, saving the expensive chef’s time. eSSD makes that pantry cheap and large. This also reduces the load placed on HBM.

This analogy is the investment map for this post. Money flows to wherever the cost per token goes down.

4. Priority 1: Custom ASIC / AI Networking — Broadcom, Marvell, TSMC

The most direct global stack for lowering token cost is custom ASIC and AI networking.

When a hyperscaler uses a chip tailored to its own workload (a custom ASIC), it does the same job more cheaply and with less power than a generic GPU. AI networking is what ties those chips into a cluster. Both lift tokens per watt directly.

A caution, though. This is already the consensus long the market loves most. A great company and a great price are not the same thing. And custom ASIC revenue is concentrated in a few large customers, so a single customer’s order swings can move the numbers sharply.

5. Priority 2: HBM + eSSD + KV-cache Storage — Samsung Electronics, SK Hynix, Micron

In inference, the real bottleneck is not raw compute but memory bandwidth and cache management. It is exactly the chef-belt analogy from Section 3.

• HBM is the ultra-fast belt right next to the chef.
• eSSD and KV-cache storage are the pantry where frequently used ingredients are pre-stacked. They avoid repeating the same computation, save expensive GPU time, and ease the load on HBM.

Samsung Electronics’ 1Q26 results materials cited demand for DDR5, SOCAMM2, PCIe Gen6 eSSD, and KV-cache storage. That lineup maps precisely onto the token-cost thesis.

For a deeper discussion of Samsung Electronics, see Samsung Electronics Stock-Bonus Buyback Analysis and the Samsung Electronics Deep Dive; for SK Hynix, see the SK Hynix Deep Dive.

If forced to pick one, the most balanced Korean exposure to the token-cost thesis is Samsung Electronics. SK Hynix is cleaner as pure HBM beta, but across KV-cache, eSSD and SOCAMM2 Samsung spans more layers — and the market crowding is lighter on Samsung than on SK Hynix.

6. Priority 3: Advanced Packaging / FC-BGA / MLCC / Silicon Capacitor — Samsung Electro-Mechanics, Daeduck

No matter how good a chip is, if the power wobbles and the signal breaks, effective token throughput falls. Advanced packaging, substrates and power-stabilization components are what protect power integrity and signal integrity.

• FC-BGA is the high-performance substrate that connects the chip to the board.
• MLCCs and silicon capacitors dampen transient voltage swings inside the GPU/HBM package.

A solid layer here lets the expensive chef run at full speed, producing more tokens. So while these parts are indirect, they clearly affect cost per token.

For detailed SEMCO analysis, see AI Server Passive-Component Bottleneck: A SEMCO Technical Explainer.

The direction is right. But the price is rich.

The token-cost thesis does point to packaging and substrates, but Samsung Electro-Mechanics has already priced much of that in. So in Korea, a second-order FC-BGA beta like Daeduck Electronics can be the “same direction, less stretched price” alternative — provided direct qualification and volume are confirmed first.

7. Priority 4: Compute Pricing Rail — CME, ICE

The starting point of this post, and its least-crowded second-order idea, is the compute pricing rail.

If the financialization of AI usage truly takes hold, the exchanges where that trading happens benefit structurally — just as exchanges enjoyed steady fee flows once oil futures took hold.

But this is option value, not confirmed revenue. Whether the contracts actually list, and whether volume builds, is unknown. So treat it as a “side-branch of AI financialization” — the smallest-weight, second-order idea.

8. Priority 5: Nvidia / AMD Full-Stack — The Thesis Changes

Nvidia and AMD remain core. They are not dropping out. But the thesis changes.

The old thesis was “GPUs are scarce, so whoever makes them wins.” The token-cost-era thesis is different.

Not “GPU shortage,” but “who provides the full stack with the highest tokens-per-watt and the best AI-factory economics."

Nvidia is strong not simply because its chips are fast, but because it bundles GPUs, networking and software to lower the cost per token of the entire AI factory. In this frame, the key question is not “how fast is the next chip” but “how cheaply does the next system produce tokens.”

9. Priority 6: Power Equipment — A Relative-Value Hedge, Not a Short

Here is a common misconception worth addressing. “If token-cost efficiency matters, power demand falls, so just short the power-equipment names, right?”

No. Power demand itself keeps rising.

The IEA forecasts global datacenter electricity consumption reaching ~945TWh by 2030 (roughly 2x versus 2024).

That is no basis for an outright short on power equipment. Simply shorting names like HD Hyundai Electric or LS Electric is dangerous.

There is only one way to use power equipment here: a relative-value hedge. Use it only, and sparingly, when all three of the following hold at once:

1. Semiconductor EPS estimates are being revised up, while
2. Power-equipment EPS estimates stall, and
3. Power-equipment valuations sit at historic highs.

In other words, it is meaningful only as a relative-value hedge against an “over-stretched power-scarcity premium.” Not an outright short.

10. Discipline: “Financialization = Buy Everything” Is Greed

This thesis is attractive. But its size and the investment decision are separate. Three things must be kept apart.

1. [Fact]: CME-Silicon Data and ICE-Ornn compute-futures plans were announced. Broadcom’s, Marvell’s and TSMC’s numbers were reported. The IEA’s 945TWh forecast is public.
2. [Inference]: Once usage gets a price, the industry shifts from a performance race to a cost-per-token race. Then the bottlenecks that lower token cost (custom ASIC, memory, packaging) are structurally favored. This is a reasonable inference from the facts.
3. [Speculation]: The SHFE futures’ contract spec, underlying-token definition, settlement method and approval timeline are all unconfirmed. Which Korean component maker supplies which customer, and at what revenue share, is also undisclosed.

In particular, the SHFE AI token futures should be treated not as “an already-listed, confirmed product” but as an early signal and option value. And specific customer/share claims — “Samsung supplies this customer’s KV-cache storage,” “Daeduck holds X% of this FC-BGA” — are not verifiable from public sources.

Even a sound thesis ends as a theme unless it is validated in the numbers.

The conclusion is not “AI is being financialized → buy all the related stocks.” It is to selectively pick the bottlenecks that actually lower the customer’s cost per token — at a price you can defend.

11. Next Checkpoints

Final Interpretation

AI token futures and GPU compute futures are still early. Nothing is listed; no volume has built. But the direction is clear.

The moment AI usage gets a market price, the battleground shifts from “a faster chip” to “a cheaper token.”

From this vantage point, what to buy is not generic AI stocks but the bottlenecks that actually lower the customer’s cost per token. Globally, custom ASIC / networking (Broadcom, Marvell, TSMC) is most direct, and memory / KV-cache storage solves the inference bottleneck. In Korea, the most balanced practical pick is Samsung Electronics — its HBM4E catch-up, DDR5, SOCAMM2, PCIe Gen6 eSSD and KV-cache storage span the token-cost stack most broadly. SK Hynix is the best quality but crowded; Samsung Electro-Mechanics has high thesis purity but a rich price; Daeduck Electronics is a second-order FC-BGA beta candidate. Power equipment is not a short — only a relative-value hedge used when the conditions line up.

The conclusion is discipline. The more AI is financialized, the more “at what price” matters as much as “what to buy.”

Fact / Inference / Speculation / Blocked

[Fact]

• Per Reuters, SHFE is early-designing AI-token-price-linked futures. The contract spec, underlying-token definition, settlement and approval timeline are unconfirmed.
• CME Group announced plans to launch compute futures with Silicon Data; ICE announced plans for GPU compute futures contracts with Ornn.
• Broadcom Q1 FY2026 AI revenue $8.4B (+106% YoY); Q2 AI semiconductor revenue guided ~$10.7B.
• Marvell FY2027 Q1 revenue $2.418B. Per Reuters, Marvell targets custom-chip revenue >$10B by FY2029.
• Per Reuters, TSMC sees chip-design focus shifting from performance to power efficiency due to AI power demand, making “tokens per watt” a key metric.
• The IEA forecasts global datacenter electricity reaching ~945TWh by 2030 (~2x versus 2024).
• Samsung Electronics’ 1Q26 results materials cited DDR5, SOCAMM2, PCIe Gen6 eSSD, and KV-cache storage demand.

[Inference]

• Once AI usage gets a price, the industry shifts from a performance race to a cost-per-token race.
• The bottlenecks that lower cost per token (custom ASIC / AI networking → memory / KV-cache → packaging / substrate) are structurally favored.
• In Korea, Samsung Electronics has the most balanced exposure across the token-cost stack; SK Hynix is the best quality but heavily crowded.
• Because power demand keeps rising, an outright short of power equipment is inappropriate; use it only as a relative-value hedge when the conditions line up.

[Speculation]

• Whether the SHFE AI token futures actually list and trade, and how the underlying token would be defined, is uncertain.
• The launch timing and liquidity of the CME / ICE compute futures are not confirmed.
• Which global customers specific Korean component makers (Samsung Electronics, Samsung Electro-Mechanics, Daeduck Electronics) supply, and at what revenue share, is undisclosed.

[Blocked]

• The SHFE futures’ contract spec, settlement method and approval timeline.
• Real-time consensus EPS revision rates and NTM valuations for the Korean names.
• Korean suppliers’ customer-level share and revenue mix in the custom ASIC / memory / packaging supply chains.

Disclaimer: For research and information purposes only. Not investment advice. Names cited are for analytical illustration; readers should perform their own due diligence and consult licensed advisors before any investment decision.