OpenAI once again takes center stage in the tech world with a deal that, if finalized, could become one of the largest capital infusions in recent industry history. The company behind ChatGPT is in talks to secure up to $60 billion from three strategic partners—Microsoft, Amazon, and Nvidia—in a move aimed at boosting its computing capacity and covering the rising costs of training and operating increasingly demanding AI models.
According to reports from various international media, Nvidia is considering an investment of up to $30 billion. Microsoft, OpenAI’s longstanding partner, is said to be contemplating investing less than $10 billion. Meanwhile, Amazon—entering as a new investor—could contribute up to $20 billion, with the possibility of exceeding that amount depending on how the deal is structured.
The interest from these three giants is no coincidence. OpenAI has become a key player in the AI value chain: its models power enterprise products, productivity tools, and cloud services, with demand for infrastructure—chips, networks, storage, and data centers—rising rapidly alongside adoption. At the same time, the market has become more competitive: the race to develop more powerful and efficient models pits OpenAI against rivals like Google and others competing for talent, energy, and large-scale computing capacity.
A Role Distribution Shaping the AI Power Map
In this potential round, each participant fits into a specific industrial logic.
Nvidia not only dominates the supply of accelerators for large-scale training and inference but also appears as a partner capable of influencing, directly or indirectly, the availability of critical hardware. Its potential investment—up to $30 billion—underscores how AI business is now inseparable from the close relationship between model software and the physical hardware that runs them.
Microsoft, for its part, has been linked to OpenAI for years, playing a key role in turning the technology into enterprise-ready services. Its consideration of an additional investment below $10 billion reflects, according to reports, a more measured approach at this stage, while maintaining a strategic position in a weapon that has become essential to its product portfolio and cloud offering.
Amazon adds a different dimension: if it does invest, it would do so in a context where cloud infrastructure and commercial agreements could weigh as heavily as capital. In other words, the money would not only strengthen its balance sheet but also solidify commitments related to capacity, distribution, and the sale of enterprise solutions based on OpenAI models.
The “Big Round” and the Real Cost of Operating Models at Scale
Behind these conversations lies a harsh reality for the entire industry: generative AI doesn’t scale for free. Training state-of-the-art models requires enormous amounts of energy, high-capacity networks, fast storage, and, most critically, ongoing access to accelerators. Even day-to-day inference has become a multimillion-dollar expense when delivering low-latency, highly available services with sustained user growth.
This has led to an increasingly common phenomenon: the same actors providing infrastructure (chips and cloud) are also becoming investors or financial partners. Such arrangements raise legitimate questions about “funding circles”: money flowing in to partly flow out later via hardware purchases, cloud consumption, or capacity contracts. This dynamic can be efficient in ensuring supply but also concentrates power among a few players and limits the maneuvering room for companies seeking diversification.
What Could Change for Businesses and the Market
If these terms materialize, the impact would extend beyond OpenAI:
- More capacity, more products: increased capital generally leads to more available compute and faster deployment cycles for new models and features.
- Deeper dependencies: the entry (or reinforcement) of infrastructure partners could strengthen technical and commercial commitments, shaping where and how large-scale models are executed.
- Ripple effect on competition and pricing: when access to chips and data centers becomes a bottleneck, preferential agreements could shift the market, especially within the enterprise segment.
- Signals to the financial market: the scale of these figures fuels a narrative—AI is perceived as critical infrastructure, not just a software trend. This attracts capital but also raises expectations.
Although no official confirmation has been made and negotiations are still subject to change, the overall picture emerging from these conversations is clear: OpenAI needs substantial financial muscle to sustain demand and compete in a cycle where advantage no longer depends only on algorithms but also on who can secure the physical resources needed for deployment first.
Frequently Asked Questions
Why is OpenAI seeking investments in the tens of billions of dollars?
Because training and operating large-scale AI models requires massive infrastructure (chips, data centers, and energy) and sustained operational expenditure to meet demand.
What does it mean that Nvidia could invest up to $30 billion in OpenAI?
It reinforces the link between hardware (AI accelerators) and models: such an investment aligns with strategies around supply, capacity, and technical collaboration in accelerated computing.
How might Amazon’s investment in OpenAI affect cloud services and enterprise customers?
It could come with commercial and infrastructure agreements, facilitating cloud capacity, integrations, and the distribution of enterprise services based on OpenAI models, depending on the final deal structure.
Why would Microsoft invest less than $10 billion if it’s already a longstanding partner?
The figures suggest a new negotiation phase where Microsoft maintains a strategic stance but adjusts its investment size relative to other participants, based on prior exposure and deal terms.