AI can't scale without cloud–the infrastructure behind the race

This week, Oracle signed a $300B cloud compute deal with OpenAI, one of the largest of its kind over a five-year span. It’s not the first though; earlier this summer, Meta signed a $10 billion deal with Google Cloud to support its AI workloads.

For those paying attention, this growing trend is an important signal – that even the most powerful tech giants can’t scale AI alone.

The reason is simple: the infrastructure demands of AI are just too great. This partnership has brought an overlooked (but critical) truth to the headlines: AI relies on cloud.

That’s right; without the cloud there is no AI advantage – no productivity gains, hyper personalisation... I could go on.

So, as organizations continue the race to harness generative AI, the spotlight is shifting from models to infrastructure. The cloud is where that race is being run.

Why AI needs cloud to ccale

Let’s explain why the cloud is so critical here. AI workloads are unlike anything we’ve seen before. Training large language models requires massive compute power (GPUs/TPUs), storage, bandwidth, and access to vast datasets.

Cloud provides the elasticity, speed, and global reach to meet these demands. The alternative, on-premises infrastructure simply can’t keep up with the pace of AI development.

Research cycles are short. Deployment needs are global. Scaling from prototype to production requires infrastructure that can flex instantly.

Without cloud, the AI boom would have hit a wall, well before we all started playing around with ChatGPT – it's the workhorse pulling the whole show along in the background.

Why cloud providers are betting big on AI

Hyperscalers – the biggest providers of cloud platforms and services; your GCP and your AWS – are investing billions to move business AI use forwards. Every major provider is building out AI-optimized infrastructure:

• Custom silicon (like AWS Trainium and Inferentia, or Google’s TPUs)
• High-performance networking to reduce training time and latency
• Integrated AI platforms like Vertex AI, SageMaker, and Azure AI Studio

So, the battleground for cloud supremacy has shifted. It’s no longer just about compute or storage – it’s about who can offer the most powerful, scalable, and developer-friendly AI stack. Cloud vendors are racing to become the default platform for AI innovation.

The future is AI–cloud symbiosis

We’re entering a new phase of convergence between AI and cloud.

We can continue to see growing adoption of training and inference within managed cloud services. AI offerings will likely become industry-specific AI solutions, with clouds infused with even more domain-relevant models.

Competitors will be more often forced to become collaborators; rivals like Meta and Google will buddy up when infrastructure needs outweigh competitive concerns. CapEx in the Cloud world will also surge, as vendors pour resources into meeting rising AI demand.

All of this matters, and will have some knock-on effects for almost everybody. Rather than a passing trend or even an isolated shift, it’s a structural recalibration in how digital innovation happens.

Infrastructure is the new differentiator

The internet needed broadband to scale – without it, there wasn’t a platform, an environment in which to grow. Likewise, AI needs hyperscale cloud.

Businesses who win the ‘AI arms race’ won’t just be those with the smartest models; they’ll also need access to the most flexible, scalable, and AI-optimized infrastructure.

The Meta-Google deal is a reminder that in the age of AI, infrastructure is a deciding factor that can spell success or failure – regardless of other investments.

For businesses in every sector, this is a reminder to look closely at how your choice of cloud provider impacts your ability to scale AI initiatives.

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