Oracle’s Surprise Role in the AI Economy

For years, Oracle was seen as a laggard in cloud computing. The market belonged to Amazon, Microsoft, and Google. Yet suddenly, Oracle is at the centre of conversations about AI. How did that happen, and what does it reveal about where capital is flowing in this new technological cycle?

From Distribution to Computation

In the 2000s and 2010s, internet businesses thrived on distribution advantages. Whoever could capture users at scale, cheaply and virally, became the locus of capital accumulation. The logic was downstream.

AI changes that logic. The bottleneck is no longer distribution but production: the ability to run models at scale. This demands chips, energy, and high-capacity datacentres. Value moves upstream, towards those who can remove the bottlenecks of computation.

Oracle’s “Old” Architecture Finds New Relevance

Oracle’s cloud never matched the reach of AWS or Azure. But its architecture, optimised for large enterprises and classic databases, emphasised deterministic performance: machines talking to each other with predictable speed, low jitter, and reliability.

For a long time this looked like a relic. Now, in the context of AI training, it looks like foresight.

The Latency Confusion

The word “latency” causes confusion.

• AWS, Google, Microsoft solved latency at the edge: delivering global networks that serve users quickly wherever they are.
• Oracle solved latency inside the cluster: ensuring GPUs and databases communicate consistently.

Both are “low latency,” but in very different senses. This distinction matters, because it shapes what Oracle is good for.

The most interesting thing in tech: how did Oracle become the “it company” in AI? It’s an interesting story about how something old became valuable to something new. | Nicholas Thompson | 12 comments

The most interesting thing in tech: how did Oracle become the “it company” in AI? It’s an interesting story about how something old became valuable to something new. | 12 comments on LinkedIn

LinkedInGowrishankar J.

Training vs. Inference

Training is the clear fit. When vast model runs demand stability across thousands of GPUs, Oracle’s deterministic design shines.

Inference, however, is less clear. Many inference workloads benefit from proximity to the edge, something Oracle has never excelled at. The current narrative tends to blur these use cases together, but they are structurally different.

A Symmetry with Nvidia

There’s a symmetry here with Nvidia. What once looked like a niche for gamers (graphics cards) became the substrate of machine learning. What once looked like a legacy database company is now a sought-after partner for AI scale-ups.

In both cases, the architecture of “the old” unexpectedly underpins “the new.”

Closing Reflection

Whether Oracle can translate this moment into lasting advantage is another question. Training may suit its infrastructure, but inference will test its limits.

Still, the deeper point remains: capital is flowing upstream. AI rewards those who can provide energy, interconnects, and reliable performance at scale. Oracle’s surprise return reminds us that in every technological cycle, yesterday’s overlooked assets can suddenly become tomorrow’s bottlenecks.

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