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JPMorgan Chase's deployment of AI tools to over 200,000 employees marks a watershed moment - not just for the financial giant, but for enterprise AI adoption writ large. The bank's massive AI implementation on AWS highlights both the immense potential and daunting infrastructure challenges as organizations push to scale AI. With nearly 1,000 applications now running on AWS, including core banking services, JPMorgan demonstrates how AI implementation is moving from experimentation to widespread deployment.

Power hungry

The computational requirements are staggering. Global AI processing is projected to consume 40 gigawatts of power by 2026 - equivalent to eight New York Cities. This isn't just about training large language models anymore; inference workloads from deployed AI systems are becoming a major driver of computing demand.

Meta has just announced a $10 billion data center in Louisiana while simultaneously pursuing nuclear power agreements to fuel its AI ambitions. The company aims to add 1-4 gigawatts of new nuclear capacity in the US by the early 2030s. They're not alone - Amazon, Microsoft, and Google are all scrambling to secure sustainable power sources, recognizing that traditional grid power may not suffice to power the compute necessary.

Infrastructure innovation

The push for AI-ready infrastructure is driving fascinating technical innovations. Schneider Electric and Nvidia have partnered to create data center designs supporting liquid-cooled AI clusters up to 132 kW per rack. The design introduces advancements in power management and cooling efficiency that could set new standards for AI infrastructure.

The ripple effects extend to telecommunications. Verizon achieved 1.6 terabit-per-second transmission speeds on its metro fiber network, covering 118 kilometers with 10 network hops. This breakthrough reduces space and power consumption by 50% per bit while delivering the bandwidth needed for real-time AI applications.

The chip race

Jensen Huang, Nvidia's CEO, frames AI infrastructure as essential for national technological independence, conducting high-level meetings with government officials globally. Yet while Nvidia remains dominant, viable alternatives are emerging. AMD's MI300 chip projects $5 billion in first-year sales, while Amazon advances its Trainium chip. In addition, the development of new AI chips in Austin highlights Texas’s growing importance as a semiconductor innovation hub.

Strategic implications 

This infrastructure build-out carries profound strategic implications. Companies must now think beyond just AI models and applications to consider the full stack of infrastructure requirements:

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In Bagel’s most recent article, they reveal how large language models are evolving from prediction tools to cognitive agents. From breaking down math problems to understanding everyday logic, this is AI's next frontier.

Key takeaways:

In other words, Bagel’s article explains how AI is evolving from simple prediction tools to smarter systems that can reason through problems and make better decisions in real time.

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