OpenAI’s AI Targets Could Consume as Much Power as India
OpenAI CEO Sam Altman has outlined one of the most ambitious infrastructure targets in the history of technology: achieving 250 gigawatts of AI compute capacity by 2033. This level of energy consumption is equivalent to the entire electricity usage of India, a country of 1.5 billion people.
A Jump from Gigawatts to Nation-Scale Energy
Altman’s projection dwarfs today’s cloud infrastructure. Microsoft Azure operates at roughly 5 GW of effective computing power, while OpenAI expects to close 2025 with just over 2 GW. Reaching 250 GW would mean scaling capacity by a factor of 125 within eight years.
For perspective, 250 GW is comparable to the output of 250 full-scale nuclear reactors — an energy footprint unprecedented in the AI industry.
Tens of Millions of GPUs and Trillions in Infrastructure
Achieving this target would require approximately 60 million Nvidia GB300-class GPUs running continuously. The associated power networks, cooling systems, and substation expansions alone are estimated to cost more than $12.5 trillion over the next decade.
The scale reflects what Altman describes as a “brutal industrialization” phase required to support the next generation of frontier AI and future AGI-level models.
Environmental Impact: Double ExxonMobil’s CO₂ Emissions
If powered by today’s energy mix, AI data centers of this magnitude would emit twice as much carbon dioxide annually as ExxonMobil, the world’s largest non-state fossil fuel emitter. This raises major concerns among environmental analysts and policymakers.
The shift may accelerate global moves toward nuclear-powered compute infrastructure and zero-carbon energy sources, a trend already visible across major cloud providers.
A Network of Mega-Contracts Behind the Expansion
OpenAI is not building in isolation. The company has already secured multi-trillion-dollar commitments from leading infrastructure partners, including $300 billion with Oracle, $100 billion with Nvidia, $90 billion with AMD, and $38 billion with AWS. These agreements highlight the scale of the ecosystem required to sustain future AI demand.
The plan signals a future where data centers become as critical to global power grids as traditional heavy industry — and potentially just as resource-intensive.
Conclusion
Altman’s 250-GW vision reveals a future where AI compute rivals national energy systems, requiring global-scale engineering and radical investment. Whether this path is sustainable, both economically and environmentally, remains one of the defining questions for the next decade of AI development.
Editorial Team — CoinBotLab