The biggest risk facing the AI industry may not be that artificial intelligence fails to live up to its promise, but that it becomes too cheap to support the extraordinary sums being spent to build it.

OpenAI and Anthropic are committing enormous amounts of capital to computing power, data centers, chips and other infrastructure in a race to remain at the frontier. At the same time, Chinese developers such as DeepSeek, Alibaba’s Qwen, Zhipu AI and Tencent are narrowing the performance gap while offering models at substantially lower costs.

That combination is raising a difficult question for investors: what happens to the economics of the frontier AI business if customers can obtain sophisticated models without paying the premium required to support the industry’s enormous infrastructure buildout?

Scott Wilson, whose early investment in SpaceX generated a multibillion-dollar windfall for Washington University’s endowment, believes the answer could be painful for the industry’s most highly valued companies.

“These trillion-dollar-plus frontier companies are not worth the liabilities that they signed up for,” Wilson said. “There’s going to be a ton of free alternatives.”

Wilson’s argument challenges one of the central assumptions behind the AI investment boom: that spending more than competitors on infrastructure will necessarily create a durable competitive advantage. His view is that the opposite could happen if advances in open-weight and lower-cost models make AI increasingly difficult to monetize.

The leading AI companies are spending hundreds of billions of dollars on computing capacity, data centers, chips, and related infrastructure in an effort to maintain an advantage over competitors. The underlying assumption is that superior models will command enough revenue to justify that investment.

But if increasingly capable models become available at dramatically lower prices, the economics could change. Customers may become less willing to pay premium prices for proprietary systems if open-weight or lower-cost alternatives deliver comparable results.

That is the scenario Wilson believes the market is underestimating.

The China Challenge Is Changing The Economics

Wilson’s skepticism has hardened as Chinese model developers have narrowed the performance gap with US frontier laboratories. Companies including DeepSeek, Alibaba’s Qwen, Zhipu AI and Tencent have emerged as credible alternatives, while open-weight models have made it easier for businesses to deploy AI without relying entirely on the largest US providers.

Wilson said conversations with colleagues operating in China have reinforced his view that progress in open-weight models is happening rapidly.

He has also been hearing similar signals from companies in Washington University’s investment portfolio.

“Whenever we talk to our portfolio companies, especially the ones who are heavy of AI, they are all moving aggressively towards open source,” he said. “It’s like any other high-cost U.S. good that has to compete with a low-cost import, particularly from China.”

That comparison captures the heart of Wilson’s thesis. If AI models become increasingly commoditized, the economics could begin to resemble other industries in which a high-cost producer struggles to maintain pricing power against a lower-cost competitor. The issue would not necessarily be whether US laboratories can build better models, but whether the incremental improvement is large enough for customers to justify paying substantially more.

OpenRouter data provide some evidence of the shift, although the platform represents only one slice of the AI market.

DeepSeek accounted for 25.3% of text-model requests on OpenRouter, compared with 18.6% for OpenAI and 2.9% for Anthropic.

The figures do not establish overall market share or revenue, and OpenRouter users are not necessarily representative of enterprise AI customers. They nevertheless illustrate the growing willingness of developers to experiment with alternatives to the dominant US laboratories.

That creates a difficult proposition for OpenAI and Anthropic. Their enormous capital requirements are based partly on the expectation that customers will continue to value frontier performance enough to sustain premium pricing.

If the performance gap narrows faster than the cost gap, that assumption becomes harder to defend.

Khosla Sees Infrastructure As The Moat

Vinod Khosla, the billionaire venture investor and early OpenAI backer, sees the economics very differently.

He believes that Wilson is focusing too heavily on the model itself and not enough on the infrastructure required to build, train, and operate it.

“People like that are silly, and they don’t understand how this works,” Khosla said when told of Wilson’s view. “They have this notion that the model is the value.”

Khosla’s argument is that a closed-model company can eventually control more of the technology stack, allowing it to lower its underlying costs even if competitors offer models for less.

That stack includes computing infrastructure, electricity, data centers, chips, software, and inference systems.

OpenAI, for example, is working on its own inference hardware, including its Jalapeo inference chip, which Khosla says could reduce the company’s dependence on Nvidia and third-party cloud providers.

“I’m not talking price, I’m talking about cost,” he said. “From power to data center to chips, to infrastructure software to inference models, the cost of the stack is almost certainly going to be lower in closed-source models than open-source.”

This is the central counterargument to Wilson’s thesis.

An AI company may be able to charge less for its models while still maintaining attractive margins if it can lower the cost of producing each response. Owning or co-designing critical infrastructure could give frontier laboratories advantages that are not visible when models are compared simply on headline API prices.

The difference is between price and unit economics.

A cheap open-weight model can be attractive to customers, but running that model may still require expensive computing infrastructure. A vertically integrated AI company could potentially offset higher model development costs by lowering its cost per inference through specialized chips, optimized software, and dedicated data centers.

If Khosla is right, today’s infrastructure spending is not simply an expense. It is the foundation of a cost advantage that could become more important as AI usage scales.

The Real Test Is Whether AI Becomes A Commodity

The disagreement exposes two competing visions of the AI industry. Wilson’s thesis assumes that model capabilities will converge quickly enough that the model itself becomes difficult to monetize. Under that scenario, customers will have little reason to remain locked into expensive proprietary systems when cheaper alternatives are available.

Khosla’s thesis assumes that the leading laboratories will maintain meaningful technological advantages while simultaneously gaining control over the infrastructure underneath their models. That could allow them to reduce costs, improve performance, and retain pricing power.

The outcome depends heavily on the pace of commoditization.

If models become interchangeable, the enormous capital commitments made by OpenAI and Anthropic could become problematic. The companies would have to generate enough revenue to service infrastructure commitments even as customers demand lower prices.

If frontier models remain materially better, however, the infrastructure race could reinforce their lead. Training sophisticated models requires enormous amounts of computing power, and the companies with access to the largest pools of capital could maintain advantages that smaller competitors cannot easily reproduce.

There is also a middle scenario in which the model layer becomes cheaper while value migrates elsewhere.

AI customers may ultimately care less about which laboratory produced the underlying model and more about applications, proprietary data, distribution, workflow integration, and reliability. In that world, model providers could face falling margins even as the broader AI economy continues to grow.

The implication is expected to be significant for companies valued on the assumption that model development itself will capture a large share of the industry’s eventual profits.

OpenAI And Anthropic Face A Different Test From Their Investors

The debate comes at a critical point for both companies. OpenAI has already demonstrated that investors are willing to assign enormous value to its position at the center of the AI ecosystem. Anthropic, meanwhile, is expected to go public next month, putting its financial model under much closer scrutiny from public-market investors.

Then, the question will not simply be whether the companies can produce increasingly capable models. It will be whether those models can generate enough recurring revenue and gross profit to justify the capital required to develop and operate them.

For now, the market has largely rewarded the spending race. The biggest laboratories have attracted extraordinary amounts of capital, while chipmakers, cloud providers and data-center operators have benefited from the resulting infrastructure buildout.

But a large infrastructure footprint is only an advantage if demand and margins grow quickly enough to absorb it.

Wilson’s SpaceX investment exemplified the power of being early to a technological transformation. His current argument is effectively that investors may be making the opposite mistake with AI: paying enormous prices for companies before the economics of the industry have been established.

Khosla believes those economics are precisely what the infrastructure race will establish.

Anthropic is committing $11.6 billion over seven years to Akamai’s cloud infrastructure, a deal that highlights the extraordinary scale of computing resources now being secured by leading AI laboratories and points to a less visible constraint in the industry’s expansion: demand for conventional CPUs.

The agreement, announced by Akamai on Thursday, is more than six times the size of the $1.8 billion arrangement between the companies reported in May. It is also the largest contract in Akamai’s history.

The scale of the commitment illustrates how aggressively Anthropic is building computing capacity as it develops and deploys sophisticated AI systems. While much of the industry’s attention has focused on the enormous demand for Nvidia GPUs and other specialized AI accelerators, Anthropic’s agreement with Akamai highlights the growing importance of general-purpose processors as AI agents perform more tasks beyond simply generating text or images.

The deal is not unconditional. According to an Akamai securities filing, the agreement depends on the company satisfying specified delivery and service-availability requirements, and either party can terminate the arrangement under certain circumstances.

The headline $11.6 billion figure represents a long-term commitment rather than revenue that Akamai will immediately recognize. Akamai expects to generate between $150 million and $300 million from the agreement in 2027, beginning in the second half of the year. Revenue is expected to reach an annualized pace of roughly $1.7 billion by the end of 2028.

For Akamai, the contract represents a major expansion opportunity but also requires a substantial upfront investment.

The company expects to spend about $5.5 billion to build the capacity required for Anthropic. It is also adding about $1.7 billion to its 2026 capital expenditure to secure components, including memory, ahead of demand. That creates a striking financial equation: Akamai is effectively committing billions of dollars of its own capital to prepare infrastructure for a customer whose payments will arrive over several years.

The Akamai agreement is the latest example of Anthropic securing computing capacity from multiple infrastructure providers as it scales its AI operations.

Anthropic has previously entered major arrangements involving Amazon, Google, Microsoft and AMD. Those relationships combine access to chips or cloud infrastructure with investments in Anthropic, creating complex financial ties between AI developers and the companies supplying the computing power required to train and operate their models.

The Akamai transaction introduces another variation.

Instead of Akamai taking an equity position in Anthropic, Anthropic receives a warrant that could eventually give it a stake in Akamai.

Under the agreement, Akamai issued Anthropic a warrant for nonvoting preferred stock convertible into 7.7 million common shares, equivalent to as much as about 5% of Akamai’s outstanding shares at a price of $111.33 per share. About 2% is expected to vest when Anthropic makes its first payment under the agreement. Additional portions are linked to Anthropic’s future spending.

For every additional $3 billion Anthropic commits to Akamai’s cloud services, roughly another 1% of Akamai becomes available to Anthropic. If all the additional spending milestones are reached, the overall arrangement could expand by as much as $9 billion, taking the potential value of the cloud relationship to about $20 billion.

The structure effectively links Anthropic’s growing computing requirements with an increasing potential ownership interest in its infrastructure supplier.

It is also the first time Akamai has attached a warrant to a cloud agreement.

A Different Kind of AI Infrastructure Deal

The arrangement reverses a structure that has become increasingly common across the AI industry. Typically, infrastructure companies invest in the AI laboratories that become their customers. Chipmakers and cloud providers have provided capital to AI developers while simultaneously securing demand for their hardware and computing services.

The Akamai agreement works in the opposite direction. Anthropic, the customer, receives the potential equity upside in the infrastructure provider, resulting in an unusual alignment of interests.

Anthropic has an incentive to increase its use of Akamai’s infrastructure because greater spending can unlock additional equity. Akamai, meanwhile, receives a large multiyear commitment that can help justify the capital expenditure required to build the necessary capacity.

AMD used a related structure with OpenAI last year, linking warrants to milestones for chip purchases.

The growing prevalence of these arrangements illustrates how difficult it has become to separate the financing of AI companies from the economics of the infrastructure supporting them.

The AI laboratory needs enormous amounts of computing capacity. Infrastructure providers need sufficiently large and predictable customers to justify building that capacity. Equity-linked contracts can tie the two sides together more closely than conventional supplier agreements.

CPUs Emerge As An Overlooked Bottleneck

The Akamai deal is notable for another reason. The agreement is centered on cloud infrastructure and highlights demand for CPUs, rather than focusing exclusively on the GPUs that have dominated the AI infrastructure narrative.

CPUs are general-purpose processors used for a wide range of computing tasks, including running code and managing web activity. As AI agents become capable of carrying out longer and more complicated sequences of tasks, demand for conventional computing resources can increase alongside demand for specialized AI accelerators.

Akamai did not disclose precisely how Anthropic plans to use the CPUs covered by the agreement. Still, the transaction points to an important feature of the AI buildout. Training and running advanced models requires much more than accelerators.

Data must be processed and moved. Applications need to execute code. Agents need to interact with websites and software. Systems need storage, networking, and conventional compute resources to coordinate the enormous volumes of work generated by AI applications.

As agentic AI becomes more widely deployed, those supporting workloads could become a substantial infrastructure market of their own. That gives Akamai an opportunity to participate in AI infrastructure without competing directly with the companies supplying the most prominent training accelerators.

The Economics Are Becoming Harder to Ignore

The agreement also illustrates the enormous capital requirements created by the AI boom. Akamai expects to spend roughly $5.5 billion building capacity for a contract that will generate an estimated $150 million to $300 million of revenue in 2027 and an annualized $1.7 billion by the end of 2028.

The gap between upfront investment and eventual revenue demonstrates why AI infrastructure providers increasingly need long-term commitments from large customers before they can justify massive capacity expansions.

It also raises questions about utilization.

Analysts have noted that if demand from AI laboratories continues expanding at the pace assumed in these contracts, the infrastructure investments could generate substantial recurring revenue for providers such as Akamai. If AI demand grows more slowly, however, infrastructure companies could find themselves carrying large capital commitments for capacity that is not fully utilized. That issue is becoming more significant as multiple cloud providers, chipmakers and data-center operators simultaneously expand capacity in anticipation of AI demand.

Anthropic, for its part, is effectively locking in access to infrastructure years ahead of time. That can provide greater certainty over capacity as competition for computing resources intensifies, but it also creates long-term financial obligations.

The arrangement therefore says as much about the economics of the AI infrastructure race as it does about Anthropic’s own growth.

Akamai’s stock rose as much as 17% in after-hours trading on Thursday, according to The Wall Street Journal, indicating that investors viewed the agreement as a significant commercial opportunity.

The market response is understandable given the size of the contract relative to Akamai’s existing business. The company now has a large customer commitment that can support the expansion of its cloud infrastructure while potentially increasing the scale of its AI-related operations.

But the deal also places greater importance on execution. Akamai must spend billions of dollars to deliver the capacity, meet service requirements, and ultimately convert Anthropic’s commitments into recurring revenue.

The warrant adds another layer to the arrangement. If Anthropic’s spending increases and additional portions vest, the company could become a meaningful shareholder in Akamai, aligning its financial interests with the success of the infrastructure provider. That structure is unusual enough to signal how quickly traditional relationships between technology suppliers and their customers are changing.