May 2026 marked Google's pivot to agentic AI with Gemini 3.5 and Omni models. The company rolled out proactive features across Search, Android, health tracking, and new hardware designed specifically for these capabilities.
Everyone's focused on GPUs for AI, but the shift to autonomous agents is quietly turning compute on its head. The bottleneck isn't model inference anymore—it's orchestration, sandboxing, and tool execution. All CPU workloads.
The enterprise AI adoption crisis isn't a model quality problem—it's an architecture problem. IBM's production data from mainframe modernization to compliance automation shows that intelligent agent logic reduces token consumption by 15-30× while improving performance.
Google's new Gemini 3.5 Flash model is matching or beating flagship models from OpenAI and Anthropic in several benchmarks—while delivering tokens 4x faster at a fraction of the cost. The performance gap between 'fast' and 'frontier' models is closing.