Investors have largely assumed model providers and hyperscalers are poised to capture significant AI-generated value, but early signs of shifting economics may suggest otherwise.

Today we’ll explore how enterprises are increasingly looking to control costs, create value, and what it means for investors.

The companies building the models and owning the compute will ultimately capture most of the value. OpenAI, Anthropic, xAI, and others are broadly viewed as the ultimate beneficiaries. Hyperscalers are committing hundreds of billions of dollars to build the infrastructure required to support them. The market has largely accepted the idea that the owners of the models and compute will be the primary beneficiaries of the AI revolution.

This is an assumption that deserves a closer examination.

AI increasingly looks like a technology that will create tremendous value for society, but what if the underlying economics evolve differently than many investors currently expect?

Does this point toward a future where models and compute become increasingly commoditized ? Will the real successes end up being the implementation of AI rather than the companies trying to sell it ?

The shift in pricing has been notable.

Companies viewed AI much like traditional enterprise software. The company paid a monthly fee, employees gained access, and costs were relatively predictable.

That is changing quickly as most AI services are priced on consumption.

At that point, AI stops looking like software with fixed costs and starts behaving more like a utility where usage directly drives expense. We are already beginning to see the implications. Uber has been one of the clearest examples, with technology news site.

The Information suggesting AI usage scaled quickly enough to consume what had been expected to be a full-year budget in just a few months. What appeared manageable during experimentation became a much larger operating expense once adoption scaled across the organization.

While companies are still early in their adoption curves. As costs rise in the early days, they will look for efficiencies, impose controls, build out more infrastructure themselves, to improve overall economics.

As AI becomes a core business capability, enterprises gain incentive to control more of these economics. Some may choose to build infrastructure themselves. Others may increasingly optimize around lower-cost models as capabilities converge.

A recent report from Axios that Microsoft has explored using lower-cost DeepSeek models to support portions of Copilot illustrates this trend. Whether DeepSeek ultimately becomes material to Microsoft’s long-term strategy is less important than the fact that cost is now being actively optimized alongside capability.

Coinbase has taken this idea a step further.

CEO Brian Armstrong has stated on X that the company increasingly routes AI workflows to lower-cost models when they are sufficient for the task, reserving frontier models for only the most demanding use cases. According to Armstrong, that approach has helped Coinbase keep AI spending roughly flat even as token usage has continued to rise.

Taken together, these examples suggest to us that a broader structural shift may be underway.

Enterprises are likely to optimize for economics as much as performance. The most advanced model will not necessarily be the default choice. Increasingly, it becomes a specialized tool for a narrower subset of high-value tasks. In that environment, model providers may find themselves competing not only on capability, but increasingly on price and cost efficiency.

The challenge is that many model providers are implicitly relying on strong pricing power to justify their long-term economics.

The industry has been built on the expectation that near-term losses can eventually be converted into high-margin revenue. Willingness from customers to pay premium prices even as alternatives improve and internal capabilities become more sophisticated.

The same logic applies to hyperscalers aggressively building capacity to sell externally.

There is no question that AI demand is real and expanding rapidly. The key question is whether supplying compute at scale will remain a structurally attractive business over time. The economics may begin to resemble those of traditional commodity markets.

Oil, natural gas, and electricity are all important. Entire economies depend on them. But importance alone does not guarantee profitability.

When supply expands aggressively and is increasingly interchangeable, competitive forces tend to compress .

A recent report from Bloomberg that Meta has explored selling excess compute capacity to external customers may also prove instructive. While the move, which has not been confirmed by Meta, could reflect an effort to better monetize existing infrastructure.

This raises the possibility that some hyperscalers may be building ahead of internal demand assumptions.

Hyperscaler is currently investing aggressively to ensure future demand is met. That level of investment may be entirely rational in a growing market.

If compute becomes abundant, models converge in capability, and enterprises increasingly internalize AI workflows, the industry could begin to shift toward something closer to infrastructure and away from software-like economics.

Infrastructure can be large, essential, and durable, but it rarely supports sustained premium valuation multiples as capital intensity remains elevated.

Some examples to consider:

Banking: JPMorgan Chase estimates productivity gains among software engineers using internal AI coding tools could unlock over $1 billion in potential organizational value, according to reporting by Reuters.

Machinery: Deere and Company stated that its AI-powered See & Spray technology has reduced herbicide usage by nearly 60 percent, materially lowering input costs for customers while improving precision.

Health Care: HCA Healthcare has announced the deployment of AI across documentation, staffing, and revenue cycle management to reduce administrative burden and improve operational efficiency.

The common thread - value accrues not from selling models or renting compute, but from using AI to improve productivity, reduce costs, enhance customer experience, and make better decisions.

These companies don’t necessarily need the most advanced models or infrastructure—they just need to apply AI where it matters.

Value isn’t captured primarily by the producer, but by the systems built on top of it. If that occurs, the companies who capture the majority of the economics may not be the ones selling AI. They may be the companies using it effectively. returns over time.

If you have any questions or comments, please feel free to let me know.