As you build agentic workflows, traditional architectures are being pushed to their limits. Internally at Google, we’re seeing four fundamental shifts in the infrastructure:
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Co-designed foundations: To push the limits of performance, hardware and software teams cannot work in silos. The best teams are building custom chips in parallel to new model and software architectures.
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Making agentic scale economically viable: Enterprises are shifting from massive training runs to fine-tuning smaller models or MaaS (Model as a Service) for agentic workflows.
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Designing predictive orchestrators: Agentic applications will force orchestrators to evolve from reactive frameworks into predictive, learning-based scheduling.
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Prioritizing sustainability: Balance the exponential surge in AI compute demand by optimizing for performance-per-watt and carbon efficiency, not just raw power.
Navigating these shifts demands a purpose-built foundation, and that’s why we built AI Hypercomputer, which includes custom silicon innovations like our 8th-generation TPUs—to provide the optimized efficiency and scale needed to power this next era of computing.
Watch our Next '26 session, The future of AI infrastructure, to dive deeper into these four pillars and see where the industry is headed next.
