Elon Musk raised doubts about the construction timeline for a major Ohio data center project backed by Nvidia Corp (NASDAQ: NVDA) and intended to support OpenAI’s growing artificial intelligence infrastructure needs. Musk posted on X that the facility will take "much longer to bring online than they think," casting skepticism on the aggressive rollout plans tied to a massive financial commitment from the chipmaker.

Nvidia Caps Ohio Data Center Commitment at $105 Billion

According to documents filed with the Securities and Exchange Commission, Nvidia’s aggregate payment obligations under the initial commitment are capped at $105 billion. These payments are subject to specific conditions, including the data center meeting applicable ready-for-service requirements. The first commitments are expected to begin in 2028.

The sprawling data center is being developed by SB Energy, an entity owned by SoftBank Group (OTC: SFTBF) (OTC: SFTBY). OpenAI has agreed to lease the site for 20 years, while Nvidia will serve as its exclusive chip provider. In addition to the lease guarantee, Nvidia plans to invest $1.5 billion in SB Energy. This follows a $1 billion investment from OpenAI and SoftBank earlier this year.

The Ohio campus is expected to eventually support as much as 8 gigawatts of power capacity. The initial phase targets 800 megawatts coming online in 2028.

Why Musk’s Warning Matters

Musk’s skepticism arrives as his own companies accelerate the build-out of massive AI computing infrastructure. Earlier this month, Musk stated that Space Exploration Technologies Corp (NASDAQ: SPCX) would exclusively use Nvidia GPUs. SpaceX is targeting roughly 10 gigawatts of AI compute capacity by the end of 2027, a significant increase from its current capacity of about 1.4 gigawatts.

Nvidia’s AI Infrastructure Push

The Ohio project underscores Nvidia’s increasingly central role in the AI infrastructure boom. The company’s data center revenue reached $75 billion last quarter, marking a 92% year-over-year increase. Last week, Nvidia announced a partnership with six major Wall Street asset managers to unlock more than $500 billion in financing for AI infrastructure.

Nvidia closed Tuesday at $219.74, down 2.34%. The shares slipped another 0.21% to $219.28 in after-hours trading, according to Benzinga Pro. According to Benzinga Edge Rankings, Nvidia ranks in the 99th percentile for growth and maintains positive short-, medium- and long-term price trend ratings.

Nvidia Corp (NASDAQ: NVDA) is refining its strategy for AI infrastructure by emphasizing the enduring value of its existing hardware alongside its next-generation systems. The company aims to convince customers that older GPUs remain productive and profitable assets, reducing the perceived urgency for immediate upgrades while maintaining demand for new technology.

Strategic Shift in Messaging

The chipmaker is promoting its upcoming Vera Rubin systems while simultaneously validating the utility of previous generations. This approach addresses a market dynamic where demand for AI computing infrastructure outstrips supply, making access to any GPU valuable. By positioning older hardware as sufficient for many inference and production workloads, Nvidia targets cost-conscious enterprises expanding beyond hyperscalers.

CEO Jensen Huang recently articulated this stance on X, stating that the "mighty A100 fleet are mission-capable from 2020 through 2029." He emphasized that Nvidia computing extends beyond physical chips, relying on the CUDA platform to allow developers to continually upgrade Ampere, Hopper, and Blackwell architectures throughout their useful lives.

The Role of CUDA

Huang described CUDA as the mechanism that makes Nvidia computing versatile and fungible. This versatility drives utilization and extends durability, transforming compute into a productive asset that is "rentable, durable and financeable." In a recent essay, he noted that AI factories possess characteristics of investable infrastructure because they produce revenue, serve a broad market, improve in performance over time, and can be redeployed.

This represents a departure from framing GPUs as rapidly aging technology. Instead, Nvidia describes AI compute as long-lived infrastructure capable of generating returns throughout its operational life.

What the Numbers Show

The strategic pivot reveals a dependency on software stickiness to sustain hardware sales cycles. By asserting that older chips like the A100 remain viable until 2029, Nvidia attempts to reconcile two conflicting objectives: persuading customers to buy latest-gen Blackwell systems while assuring them that older Ampere-based hardware holds value. This suggests that future revenue growth may rely less on forced obsolescence and more on the expanding total addressable market for AI inference workloads where older GPUs are economically sufficient.