The artificial intelligence debate is changing.
For several years, the central question was whether AI worked. Then it became whether the technology could scale.
Increasingly, the question is becoming more difficult:
Who actually captures the value?
That distinction matters because AI adoption can accelerate, token consumption can soar and the economics of individual participants can still deteriorate.
This is the point at which a technology story becomes an operating-model story.
And for logistics companies deciding where AI creates economic advantage, that may be much more useful than watching the daily valuation of another AI stock.
More Tokens Does Not Automatically Mean More Revenue
The basic AI economic equation has generally seemed straightforward.
Models become better. Better models create more useful applications. Applications increase demand. Demand consumes more tokens. More tokens create more revenue.
The complication is price.
AI inference is becoming cheaper at a remarkable rate. Competition among model providers is increasing, open-weight models are improving, specialized models are proliferating and hardware efficiency continues to advance.
That is excellent for customers.
It is less obviously excellent for every company selling tokens.
Man Group’s bearish analysis of the AI investment cycle describes the problem starkly: if token prices decline faster than inference demand expands, enormous growth in usage does not necessarily produce the revenue required to justify an equally enormous infrastructure buildout.
Goldman Sachs Research is more constructive. Its work points to sharply increasing token consumption – potentially a 24-fold increase by 2030 as agentic AI expands – while declining unit compute costs could improve hyperscaler margins.
Those positions are not actually contradictory.
They describe the variable that matters.
The economic outcome depends on the relationship among volume, price and cost.
That is unit economics.
The De-Rating Is Telling Us Something
The financial market has already become more discriminating about AI exposure.
A recent Goldman basket analysis reportedly showed an all-inclusive AI pair roughly 46 percent below its previous highs, even as underlying demand for compute remains strong. Goldman’s argument is not that AI is ending; rather, the opportunity may be broadening toward businesses with clearer monetization, embedded workflows and defensible economic positions.
That distinction is important.
A broad technology transition does not guarantee that every participant in the technology stack earns extraordinary returns.
The internet changed the world. Many internet companies disappeared.
Containerization transformed global trade. That did not mean every shipping line earned exceptional margins.
Cloud computing became foundational infrastructure. The economics nevertheless concentrated in particular layers of the stack.
AI is likely to behave similarly.
The technology can be revolutionary while the value migrates.
Open Models Are Accelerating the Pressure
The open-model transition makes this more visible.
Vercel’s AI Gateway data recently showed open-weight models reaching approximately 62 percent of token volume on August 22, up from 28.4 percent roughly two months earlier and about 11 percent in April. These are figures from one platform rather than the entire AI industry, but the speed of the shift is notable.
Customers are learning something logistics managers learned long ago.
Not every movement requires premium service.
You do not ship every load by air. You do not give every SKU the same inventory policy. You do not assign the most expensive resource to every task simply because it is technically capable of performing it.
The same logic is beginning to apply to AI.
Enterprises can route difficult tasks to expensive frontier models while sending repetitive, lower-value or highly specialized workloads to cheaper models.
This is good architecture.
It is also price pressure.
As switching becomes easier, the model itself can become one component within a larger decision architecture.
That changes where the economic moat resides.
The Value May Move Up the Stack
Consider a logistics company using AI to manage exceptions.
The model may analyze late shipments, weather, inventory availability, customer commitments and transportation alternatives. It may recommend that an order be reallocated, a shipment expedited or a customer promise changed.
Suppose the model call costs 20 cents today and two cents three years from now.
The model provider has experienced severe unit-price compression.
The logistics operator has not necessarily lost anything.
In fact, the logistics operator may gain.
If the AI avoids a $5,000 expedite, prevents a stockout or allows one planner to manage twice as many exceptions, the economic value exists primarily in the operating outcome – not in the token.
This is where the AI economics discussion becomes particularly relevant to enterprise logistics.
Falling model prices could transfer economic value away from model providers and toward companies capable of embedding inexpensive intelligence into valuable workflows.
The model becomes cheaper.
The decision becomes more valuable.
Architecture Becomes the Moat
This suggests that enterprises should be careful about defining an “AI strategy” around access to a particular model.
Model leadership can change.
Prices can change even faster.
An enterprise architecture built tightly around a single provider may eventually look like a transportation network designed around one carrier regardless of lane, service requirement or price.
The more durable architecture is likely to separate the business problem from the intelligence resource used to solve it.
Understand the decision.
Assemble the required context.
Define the constraints.
Determine the acceptable action.
Then route the problem to the model – or combination of models – capable of solving it at the appropriate cost.
That is not simply AI adoption.
It is AI orchestration.
And it looks very similar to other logistics optimization problems.
Intelligence Is Becoming a Variable Cost
The larger economic change may be that machine intelligence is becoming a purchasable input whose price declines rapidly.
That is extraordinary.
For most of industrial history, intelligence has been expensive. Analytical capacity was constrained by the number of skilled people available to perform the work.
AI begins to relax that constraint.
If the cost of a useful unit of machine reasoning continues falling, companies can economically apply intelligence to thousands of decisions that previously could not justify human analysis.
Shipment prioritization.
Carrier selection.
Inventory rebalancing.
Appointment scheduling.
Exception resolution.
Warehouse labor planning.
Supplier-risk analysis.
Customer-response generation.
Every one of these can potentially consume enormous quantities of tokens while producing economic value far larger than the cost of those tokens.
This is why collapsing AI prices are not necessarily bearish for AI adoption.
They may be extraordinarily bullish for the users of AI.
The Second Half of the AI Trade
The first phase of the AI boom rewarded scarcity.
There were too few advanced GPUs. Too little compute. Too few frontier models. Too little capacity.
Scarcity created pricing power.
The next phase may reward orchestration.
Models will proliferate. Intelligence will become cheaper. Enterprises will learn to switch among providers. Open-weight systems will handle an increasing share of routine workloads. Infrastructure costs will continue to matter, but customers will become much more disciplined about what they are willing to pay for a unit of intelligence.
That does not mean the AI boom is over.
It means the economics are maturing.
The winners in logistics will probably not be the companies that consume the most AI.
They will be the companies that turn increasingly inexpensive intelligence into better operating decisions.
That is a very different metric.
Tokens are an input.
The decision is the product.
And the economic value ultimately belongs to whoever can make that decision improve the system.
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