OpenAI Signs Compute Deals Worth Up to $1 Trillion, Reshaping U.S. Tech Power

OpenAI compute deals are the headline — and they’re reshaping U.S. tech power and vendor economics right now.

Key Points

Summary

This week OpenAI announced it has signed a series of long-term agreements to secure computing capacity to run large AI models—contracts that public reporting says total as much as $1 trillion USD (≈¥7.2 trillion RMB).

Those deals span suppliers including AMD (AMD), NVIDIA (Yingweida 英伟达), Oracle (Jiaguwen 甲骨文), CoreWeave and others, and tie parts of those companies’ future fortunes to OpenAI’s growth.

Key figures and what they mean

• Aggregate compute agreements: approximately $1 trillion USD (≈¥7.2 trillion RMB).
• Capital raised by OpenAI in the past 12 months: roughly $47 billion USD (≈¥338.4 billion RMB).
• Reported valuation after the recent funding round: about $500 billion USD (≈¥3.6 trillion RMB).
• Planned U.S. infrastructure investment under the “Stargate” initiative: up to $500 billion USD (≈¥3.6 trillion RMB).
• Estimated deployment cost per 1 GW of AI compute capacity: roughly $50 billion USD (≈¥360 billion RMB).
• OpenAI projects more than 20 GW of capacity over the next decade, an order of magnitude of computing power comparable to the electricity draw of about 20 nuclear reactors.

Deal structure highlights

The agreements are not simple vendor contracts.

In several cases they mix compute-service commitments with equity or milestone-based equity options.

For example, the reported AMD deal includes milestone terms that could allow OpenAI to buy up to 10% of AMD stock at $0.01 per share if certain project and stock-price targets are reached—an arrangement AMD’s CEO described publicly as “innovative.”

Why suppliers are participating

Chip and cloud providers see upside from locking in demand as AI model training and inference become massively capital‑intensive.

For suppliers, the agreements:

• Guarantee long-term, large-scale consumption of chips and data-center services.
• Provide upside tied to OpenAI’s commercial success via equity or milestone options.
• Create strategic partnerships that can lock in market share as AI systems scale.

Market reaction

Equity markets reacted quickly after the announcements in recent weeks.

Oracle (Jiaguwen 甲骨文) shares surged after a large OpenAI-related order, briefly lifting founder Larry Ellison (Lali Ailisen 拉里·埃里森) to the top of global wealth rankings.

AMD shares jumped dramatically on the equity‑linked transaction, adding hundreds of billions of dollars of market value in a single session.

NVIDIA (Yingweida 英伟达) has also rallied this year as investors priced in sustained, huge demand for its GPUs.

Risks and skepticism

Credit rating agencies and some investors have raised doubts.

• Profitability question marks: Rating agencies such as Moody’s noted OpenAI’s revenue model remains unproven at the scale implied by these deals.
• If OpenAI’s revenue growth underperforms, investor enthusiasm could reverse quickly.
• Execution and cost risks: Building and powering tens of gigawatts of GPU-based capacity is capital and energy intensive.
• The reported cost assumptions (≈$50 billion USD per 1 GW deployed) depend heavily on commodity pricing—power, chips, and data-center construction costs—and on continued rapid technological progress.
• Concentration risk: Many large tech providers now have material exposure to OpenAI’s success.
• Poor execution or regulatory change that limits OpenAI’s addressable market could cascade across partners.

OpenAI’s stance

OpenAI’s CEO Sam Altman (Samu Aoteman 萨姆·奥尔特曼) has repeatedly said the company prioritizes rapid investment and growth over near‑term profitability.

Quoted in coverage, Altman said profit “is not in the top ten” priorities right now—OpenAI is focused on deploying new products and scaling paid usage.

Management projects revenues in the coming years could reach the low‑hundreds of billions of dollars if paid adoption accelerates.

Strategic implications for the U.S. tech landscape

These compute deals accelerate a structural shift.

AI compute demand is concentrating capital and strategic influence around a smaller set of providers who can deliver massive data‑center scale and GPU supply.

Companies that align early with leading foundation‑model providers can capture outsized revenue growth.

They also assume shared risk tied to the platform’s commercial outcomes.

Expanded analysis — what investors, founders, and tech leaders should track

Track 1 — Execution timelines and delivery cadence.

If suppliers can’t ramp GPU shipments or data-center builds on the expected timeline, the headline dollar figures may not translate into near-term revenue or utilization.

Track 2 — Power and energy contracts.

At ≈$50 billion USD (≈¥360 billion RMB) per 1 GW deployed, power procurement and long-term energy pricing are central to cost assumptions.

Track 3 — Commercial adoption and pricing models.

The market is now betting that paid usage of large models can scale into the low‑hundreds of billions of dollars annually.

That implies aggressive productization of inference, developer platforms, and vertical-specific deployments.

Track 4 — Regulatory and antitrust signals.

Concentration of compute demand raises policy risk around market power, export controls, and data governance.

Regulators may scrutinize tie-ups that effectively bind multiple infrastructure providers to a single platform.

Track 5 — Downstream ecosystem health.

Startups building on top of foundation models depend on stable, affordable compute.

Widespread vendor concentration could increase procurement friction and pricing volatility for those companies.

Quick takeaways for different audiences

• Investors: These deals reprice the expected TAM for chips, data-center services, and cloud AI products, but execution and revenue delivery are the next big catalysts.
• Founders: Lock in flexible compute options and multi-vendor strategies to avoid single-point exposure.
• Tech leaders: Negotiate energy and long-term supply clauses into contracts; contingency plans for chip supply disruptions are essential.
• Marketers: Emphasize product differentiation and integration beyond raw model access, because platform pricing will be tightly contested.

Bottom line

Whether the headline number ($1 trillion USD / ≈¥7.2 trillion RMB) proves fully real in cash flows or is a present‑value accounting of multi‑year commitments, the scale of the agreements marks a new phase in the commercialization and industrialization of AI.

For suppliers, investors and policy makers, the questions now center on execution, cost inflation (especially power), and the durability of demand for paid AI services.

OpenAI compute deals are now a central variable in the next wave of U.S. tech strategy and capital allocation.

References

• OpenAI has signed $1 trillion in compute deals – 第一财经
• OpenAI Blog – OpenAI
• AMD Press Releases – AMD