AWS & OpenAI: A New Alliance
OpenAI will run its most advanced AI models on AWS in a $38 billion, seven-year deal, redefining who controls the infrastructure behind artificial intelligence.
TL;DR
OpenAI has signed a $38 billion, seven-year deal with Amazon Web Services (AWS) to run its most advanced AI workloads. The move shows that artificial intelligence isn’t just about clever code, it’s about huge computing power and global cloud infrastructure. For the UK and Europe, it raises fresh questions about data sovereignty, cloud dependency, and resilience.
Why it matters
AI is becoming the engine of modern business, science, and government. But every engine needs fuel, in this case, compute power. This deal between OpenAI and AWS underlines how control over computing infrastructure is now one of the biggest levers of power in the digital world.
The Story
1. What’s Happened
On 3 November 2025, OpenAI and Amazon Web Services announced a huge partnership worth $38 billion over seven years. OpenAI will use AWS’s global infrastructure to train and run its most advanced AI models, including the successors to ChatGPT and DALL-E.
According to Amazon’s announcement, the deal gives OpenAI access to hundreds of thousands of the latest NVIDIA GPUs, hosted in AWS data centres, with the capacity to scale up to tens of millions of CPU cores. Most of the new infrastructure will come online before the end of 2026, with expansion planned into 2027 and beyond.
In short: OpenAI gets the world’s biggest and most flexible AI supercomputer. AWS gets a front-row seat in the global AI boom.
2. What It Really Means
Compute is now the battleground
AI innovation used to be about smart algorithms. Now it’s about raw power. Training giant models that can reason, generate images, or even act as “digital agents” demands enormous amounts of computing. The companies that can supply this power — reliably and at scale — are the ones shaping the future of AI.
AWS re-enters the race
AWS has sometimes looked like it was falling behind Microsoft Azure (with OpenAI) and Google Cloud (with Gemini). This partnership changes that. It shows AWS can compete at the highest level — not only in size and speed, but in its approach to being open.
AWS already works with several major model builders, including Anthropic (Claude), Meta (Llama), Mistral AI, Stability AI (Stable Diffusion) and DeepSeek. Adding OpenAI to that list means AWS is turning into the builder’s platform of choice — an open ecosystem where developers can pick the best model for their needs and build AI applications across them.
By keeping its platform open rather than backing a single model, AWS is betting that being the most flexible infrastructure provider will make it the default cloud for AI innovation worldwide.
3. The UK/EU View: Sovereignty in the Cloud
For the UK and Europe, this deal highlights an uncomfortable truth: much of the world’s AI now runs on infrastructure owned and operated by a handful of US tech giants.
Under UK GDPR and the EU’s upcoming AI Act, organisations are expected to know where their data lives and who controls it. If AI models or the systems that use them, depend entirely on foreign cloud providers, that control becomes murky.
For businesses, governments, and regulators in Europe, the challenge will be to balance the convenience and power of hyperscale clouds with the need for digital sovereignty making sure critical data, algorithms, and infrastructure remain under trustworthy oversight.
4. What Businesses Should Think About
This deal doesn’t just affect Silicon Valley it will ripple across every sector using AI, from construction to healthcare.
Here’s what CIOs and digital leaders should consider now:
Check your cloud dependence. If your AI tools or data pipelines run on just one provider, review what would happen if that provider suffered an outage, raised prices, or changed terms.
Plan for scale. The next generation of AI models will demand far more compute power. Organisations may need to rethink infrastructure budgets or explore hybrid and sovereign options.
Governance and compliance. Map where your data and models are processed, and align this with UK/EU laws on privacy, accountability, and explainability.
Budget for power and resilience. Big AI equals big bills — both in compute costs and energy consumption. Model efficiency will become a major factor in sustainability and cost control.
5. Risks and Constraints
Financial exposure. A $38 billion commitment means both sides are betting big. If AI adoption slows or costs rise, returns may take years to materialise.
Chip supply. The deal depends heavily on NVIDIA GPUs, which are in global shortage. Export controls or supply issues could delay rollout.
Concentration risk — and the limits of multi-cloud resilience. While several hyperscale providers now compete, including AWS, Microsoft Azure, and Oracle Cloud, true workload distribution still depends on the resilience architecture within OpenAI itself. Multi-cloud capability isn’t automatic; it requires deep integration across infrastructure, orchestration, and data pipelines. If OpenAI can shift workloads between clouds, that would strengthen continuity for enterprises relying on its models. But if its systems remain tuned to one provider, a single point of failure persists — meaning that even with multiple providers, systemic concentration risk still exists, especially given the dependency on NVIDIA hardware.
Regulatory scrutiny. With so much AI capacity concentrated among a few American giants, competition authorities in the UK and EU are likely to take a closer look at market fairness and access.
6. What Happens Next
The next few years will likely see AWS expand data-centre capacity across the UK and Europe to meet growing AI demand. Expect new energy-intensive facilities, more GPUs, and local compliance zones designed to meet regional data-protection laws.
For OpenAI, the test will be whether this infrastructure can deliver faster, s
afer, and more reliable AI systems — without running into the scaling problems that come with size and complexity.
And for everyone else? It’s a reminder that AI doesn’t live “in the cloud”, it lives in data centres, on chips, and inside the networks that connect them. Whoever builds and controls that infrastructure will hold a key to the next era of digital power.