Google used its annual Cloud conference in Las Vegas to make a clear point: the test phase of generative AI is ending, and commercial deployment is starting.
Google Cloud chief executive Thomas Kurian said the market has moved beyond experimentation. The company unveiled “Gemini Enterprise”, a unified platform that allows businesses to build and manage custom AI agents. These tools aim to handle repetitive tasks, analyse internal data and support employees across departments.
That matters because many firms now face the same decision households make with new technology: keep doing tasks manually, or invest upfront to save time later. A finance team choosing AI for reporting mirrors a family buying a dishwasher. The cost comes first. The value appears over time.
Google also reaffirmed plans to spend $175–$185 billion on computing infrastructure in 2026, with more than half directed towards cloud-related machine learning. That level of investment signals confidence that demand will justify the expense.
The company introduced new TPU chips designed separately for training and inference. That split reflects a maturing market. Businesses no longer want raw power alone. They want lower costs, faster responses and systems that scale without waste.
Google said 75% of its new code is now AI-generated. That figure raises a sharper question: if engineers increasingly review code rather than write it, how should firms train junior talent?
Amazon and Microsoft still lead cloud market share, but Google’s strategy looks disciplined. Rather than chase every headline, it is tying AI directly to products customers already buy.
What if enterprise clients hesitate over security or unclear returns? Then expensive infrastructure could sit underused. If adoption accelerates, Google may have positioned itself for a stronger second half of the decade.
Author: Pishon Yip