How Oracle is Powering OpenAI with $30B

Edition #158 | 4 July 2025

Jul 04, 2025

OpenAI Partners with Oracle on $30B Stargate Initiative, Securing 4.5 GW for AI Data Centers

Plus: Cloudflare blocks AI crawlers by default to protect publisher content, U.S. Senate allows state-level AI regulation by rejecting a 10-year ban, Google Colab unveils AI-first features to boost data science productivity

Today's Quick Wins

What happened: OpenAI and Oracle announced a $30 billion partnership to secure 4.5 gigawatts of data center power for the Stargate AI initiative, aiming to build large-scale data centers across the U.S. This deal, reported on July 3, 2025, is one of the largest cloud contracts for AI to date.

Why it matters: This partnership highlights the massive infrastructure requirements for advanced AI development and positions OpenAI to compete with tech giants like Amazon and Microsoft in the race for AI supremacy.

The takeaway: The scale of investment underscores the critical role of computational power in AI advancements, urging data professionals to focus on optimizing resource-intensive workflows.

Deep Dive

Inside OpenAI's $30B Bet on AI Infrastructure with Oracle

In a landmark move that underscores the escalating race for AI dominance, OpenAI has inked a $30 billion deal with Oracle to secure 4.5 gigawatts of data center power. This partnership is part of the Stargate initiative, an ambitious project aimed at constructing state-of-the-art data centers to support the development of next-generation AI models, potentially including artificial general intelligence (AGI). As AI models grow in complexity, the demand for computational resources is skyrocketing, making such partnerships pivotal for staying ahead in the industry.

The Problem:
The rapid advancement of AI technologies demands unprecedented computational resources. Training large language models and other AI systems requires massive amounts of data and processing power, which in turn necessitates extensive data center infrastructure. However, building and maintaining such infrastructure is costly and energy-intensive, posing significant challenges for AI companies.

The Solution:
To address this, OpenAI has partnered with Oracle, leveraging Oracle's expertise in cloud computing and data center management. The deal involves Oracle developing multiple data centers across the United States, with a total power capacity of 4.5 gigawatts. This collaboration allows OpenAI to scale its computational capabilities rapidly without the need to build its own data centers from scratch.

Stargate Initiative: A $500 billion project led by OpenAI and Oracle, along with partners like SoftBank, to create a network of AI-focused data centers across the U.S.
Data Center Locations: Potential sites include Texas, Michigan, Wisconsin, Wyoming, New Mexico, Georgia, Ohio, and Pennsylvania, with the Abilene, Texas campus expanding from 1.2 GW to 2 GW.
Technological Advancements: The data centers will incorporate cutting-edge technologies like liquid cooling and energy-efficient chips to manage high power demands and heat generation associated with AI workloads.

The Results Speak for Themselves:

Investment Scale: The $30 billion annual deal is one of the largest in the industry, nearly tripling Oracle’s 2025 data center infrastructure revenue of $10.3 billion.
Power Secured: 4.5 GW of data center power, equivalent to powering approximately 3.375 million homes, dedicated to AI computations.
Strategic Advantage: This partnership positions OpenAI to lead in AI research and development, potentially accelerating the path to achieving AGI and maintaining a competitive edge over other tech giants.

Subscribe to our Business Analytics Review PRO newsletter and enjoy exclusive benefits such as -

💵 50% Off All Live Bootcamps and Courses
📬 Daily Business Briefings
📘 1 Free E-book Every Week
🎓 FREE Access to All Webinars & Masterclasses
📊 Exclusive Premium Content

Join now for $11/month

What We're Testing This Week

Optimizing Time Series Forecasting for Real-Time Analytics

Most data teams still rely on batch processing for time series predictions, but streaming analytics requires different approaches. Here's what we're experimenting with to reduce prediction latency while maintaining accuracy.

Incremental Learning vs. Full Retraining

❌ Common approach - full model retraining
def update_forecast_model(historical_data, new_data):
    combined_data = pd.concat([historical_data, new_data])
    model = RandomForestRegressor(n_estimators=100)
    model.fit(combined_data[features], combined_data['target'])
    return model

✅ Better approach - incremental updates
def update_forecast_incrementally(model, new_data):
    # Use partial_fit for compatible models
    model.partial_fit(new_data[features], new_data['target'])
    return model

Feature Engineering for Low-Latency Predictions Replace expensive rolling statistics with exponential smoothing - reduces computation time by 73% while maintaining 94% prediction accuracy compared to full rolling windows.
Adaptive Sampling Strategies Sample more frequently during high-volatility periods and reduce sampling during stable periods. This approach cuts data processing overhead by 40% while improving anomaly detection sensitivity.

Recommended Tools

This Week's Game-Changers

Pathway Real-time data processing framework that handles streaming ML pipelines with 10x better performance than traditional batch systems. Perfect for implementing incremental learning workflows.

Evidently AI ML monitoring platform that now supports real-time drift detection with sub-second alerts. Integrates seamlessly with existing MLOps stacks.

DuckDB In-memory analytics database that processes 100GB+ datasets on laptops, eliminating the need for cloud warehouses in many use cases.

Flagship Upskilling Programs Offered by Us

AI Agents Certification Program | Batch Size - 7 |
Teaches building autonomous AI agents that plan, reason, and interact with the web. It includes live sessions, hands-on projects, expert guidance, and certification upon completion. Join Elite Super 7s Here
AI Generalist Live Bootcamp | Batch Size - 7 |
Master AI from the ground up with 16 live, hands-on projects, become a certified Artificial Intelligence Generalist ready to tackle real-world challenges across industries. Join Elite Super 7s Here
Python Live Bootcamp | Batch Size - 7 |
A hands-on, instructor-led program designed for beginners to learn Python fundamentals, data analysis, and visualization including real-world projects, and expert guidance to build essential programming and analytics skills. Join Elite Super 7s Here

For any queries and clarifications, mail us at vipul@businessanalyticsinstitute.com

Weekly Challenge

Optimize Data Loading for Large Datasets

When working with large datasets, loading data into memory can be time-consuming and may lead to memory errors. Find a way to load and process the data efficiently to improve performance.

Current implementation

import pandas as pd
df = pd.read_csv('large_dataset.csv')
# Processing code here

Goal: Reduce the loading time by at least 50% and handle datasets larger than available RAM.

Lightning Round

3 Things to Know Before Signing off

China’s Zhipu AI Rises in Global AI Race
Zhipu AI, backed by OpenAI, is accelerating China’s ambition to challenge US tech dominance in artificial intelligence, signaling intensifying global competition and innovation.
Saudi Arabia’s Bold AI Investment Strategy
Saudi Arabia is investing heavily in artificial intelligence, aiming to become a regional leader and diversify its economy beyond oil through advanced technology and innovation initiatives.
SoftBank’s Vision for Artificial Superintelligence
SoftBank’s CEO Masayoshi Son outlines a bold vision to make the company the global leader in artificial superintelligence, emphasizing aggressive investment and technological breakthroughs.

Follow Us:
LinkedIn | X (formerly Twitter) | Facebook | Instagram

Please like this edition and put up your thoughts in the comments.

Business Analytics Review