Oracle Faces Layoffs Amid Costly AI Push

Oracle is apparently preparing to cut thousands of jobs as the company ramps up spending on AI. Or, more specifically, the infrastructure needed to support AI workloads.

The layoffs could begin as soon as this month and would affect multiple divisions, according to reports. 

The move comes as Oracle pours billions into building data centers capable of running large AI models and supporting cloud contracts with companies such as OpenAI, Meta, and xAI.

At the heart of the decision is a simple problem facing much of the tech industry right now, and one we have been highlighting with increased frequency: AI requires enormous computing capacity, and that capacity is expensive to build.

The rising cost of AI infrastructure

Oracle has been stealthily and aggressively investing in data centers to keep up with demand for AI workloads. 

Over the past year, the company has moved from a smaller cloud contender to a more prominent provider of computing power, helped in part by large infrastructure agreements tied to generative AI.

But that growth is also putting a massive strain on the money jar.

According to Reuters, Oracle is preparing to raise between $45 billion and $50 billion through a combination of debt and equity in order to expand its cloud infrastructure.

Bloomberg reported that the company is facing a cash crunch linked to the scale of those infrastructure investments, prompting a review of costs across the business.

Layoffs are part of that review.

Those could run into the thousands and touch multiple parts of the company. Some roles may also shrink as Oracle leans more on automation and internal AI tools, according to the reports.

Oracle had around 162,000 employees last year, so cuts of that size would rank among its larger workforce adjustments in recent years.

Creating capacity for the AI era

Oracle’s infrastructure push points to a broader race among cloud providers to supply the computing power required by generative AI systems.

Training and running those models requires huge clusters of GPUs, high-capacity data centers, and untold amounts of electricity. The costs can reach tens of billions of dollars before a single customer workload runs.

Oracle’s recent deals have helped position the company as a key supplier of AI compute, which is key in this race. At the same time, those commitments require rapid expansion of its cloud footprint.

For companies building software or services on top of that infrastructure, the impact tends to show up pretty quickly. More spending on AI capacity changes how computing power gets priced and delivered.

For Oracle, the focus right now appears to be building the infrastructure first and figuring out the costs afterward.

NVIDIA is also doubling down on the infrastructure side of the AI boom, recently lining up a roughly $20 billion investment in OpenAI to strengthen the partnership behind many of today’s large AI systems. This is in line with what Oracle is dealing with. Building and running AI requires massive computing capacity, and the companies supplying that infrastructure are racing to keep up.