---
id: ins_evans-token-pricing-commoditization
operator: Benedict Evans
operator_role: Independent technology analyst
co_operators: []
source_url: https://www.ben-evans.com/benedictevans/2026/7/9/ways-to-think-about-token-pricing
source_type: essay
source_title: "Ways to think about token pricing"
source_date: 2026-07-09
captured_date: 2026-07-12
domain: [ai-native, growth-demand]
lifecycle: [strategy-and-bets, pricing-and-packaging]
maturity: applied
artifact_class: framework
score: { originality: 3, specificity: 4, evidence: 3, transferability: 4, source: 5 }
tier: B
related: [ins_tunguz-inference-pricing-value-beats-cost-plus]
raw_ref: ""
---

# Every structural dynamic in AI points toward commodity pricing at the model layer, with value concentrating further up the stack

## Claim
AI foundation models are in a temporary supply crunch that resembles pricing power but is not. Every observable structural dynamic points toward commoditization: competing frontier models on similar training regimes, trillion-dollar capacity additions in progress, per-token efficiency improvements each generation, and no clear winner-takes-all dynamic at the model layer. Value will concentrate further up the stack, as it did in mobile and semiconductors.

## Mechanism
Token pricing sits between sellers' marginal cost and buyers' ROI. The supply crunch is unstable: multiple labs are building frontier models from shared scientific foundations and similar data sources; data center capex commitments are measured in trillions; and inference efficiency improves with each model generation. Inference carries 40-50% gross margins today, but those figures exclude training costs that currently exceed revenue.

Historical analog: TSMC generates $53 billion in net income, less than Apple alone, despite being the indispensable manufacturing layer beneath everything Apple makes. Cellular carriers built a trillion dollars of infrastructure over two decades while application-layer companies captured the majority of consumer and enterprise value. In both cases, the necessary infrastructure layer remained compressed while value accumulated in what ran on top of it.

## Conditions
Holds when: multiple frontier models exist with equivalent capability and a shared training regimen; supply can scale with capital investment; and application-layer products generate higher gross margins than the model API providers beneath them.
Fails when: one lab achieves a durable capability gap that capital alone cannot replicate; or when regulatory barriers limit competition among frontier model providers.

## Evidence
> "We're in a supply crunch, and this is unstable. All of the variables are in play."

> "Every dynamic we can see points to the latter" [commoditization].

> "Token price is a function of supply and demand at a level between the sellers' marginal cost and the buyers' ROI."

> "Every path to foundation models having market dominance...requires something to change."

> "All the value was captured by other people further up the stack."

Comparators cited: cellular industry at approximately $1 trillion annual revenue with $200 billion capex annually; TSMC net income at $53 billion, less than Apple alone; mobile data traffic grew several orders of magnitude over 20 years while carrier margins did not.

## Signals
- Multiple frontier models show indistinguishable benchmark performance on core tasks.
- Per-token prices fall quarter-over-quarter even before new supply capacity is online.
- Enterprise buyers route workloads across multiple model providers without friction.
- Application and tooling companies report higher gross margins than model API providers.

## Counter-evidence
Evans acknowledges genuine uncertainty: the supply crunch creates conditions where current pricing could persist if demand grows faster than supply. A lab with a durable capability gap could sustain premium pricing if no competitor closes it. Demand-side dynamics remain unclear; only software development currently shows strong product-market fit at scale, and ROI across most enterprise use cases is unproven. If demand grows faster than supply, pricing power could persist longer than the structural signals suggest.

## Cross-references
- `ins_tunguz-inference-pricing-value-beats-cost-plus`: Tunguz argued for value-based pricing on the sell side; Evans maps the structural conditions that determine whether value-based pricing is sustainable for model providers.