Summary

• Training AI models requires superfast chips, called general processing units (GPUs). These chips can run multiple tasks in parallel, which accelerates the training process.
• Agentic AI requires a different kind of horsepower: When an AI agent responds to a query, it doesn’t simply crank out a bunch of computations; instead, it reasons through a problem, reaches across databases, and coordinates with other agents.
• We believe delivering agentic AI at scale will require a new and improved information racetrack.

As the AI story moves from training large language models to delivering myriad “agentic” applications, we believe investors have an opportunity to capitalize on this crucial shift.

On April 22, Google (GOOG)(GOOGL) marked a

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