Google Cloud’s Post

future of safe ai and agi we have to to create a separate internet for ai " AI's INTERNET " Fully monitored, authenticated, on surveillance 24/7 same internet but with some restrictions limited access like every website can be customised to share what needed to do the task. when an agent start using AI's internet then the agent gets its agent ID (AGENT ID-every details about that agent, who is using, for what) without this ID no agent is permitted to use the AI's internet and for making this ID agent have to give information to the security system that what he is going to do if it is permitted to that agent then ID generates and access granted to that agent and then our system tracks the activity of the agent and if the agent commits any false action then our system flags as potential risk and freezes it and transfer it to our soc agents to verify and if true flag then permission dismissed agent blocked. and if everything goes right then after completing task permission resets to none and access denied. can vary according to the agent origin is it from trusted enterprise, script kiddie or evil agent. we can decide how much access, & this AI's INTERNET is an ultimate realtime sandbox environment for ai without loosing benefit.

Lower cost per task is the headline. Cost per exception is the number that decides the business case. The cheap run is the one that works. The expensive run is the one a person has to untangle later. Faster, cheaper models help. They help most when the failures get cheaper too. Is anyone tracking both numbers side by side yet?

The focus on cost per task is the part that matters most for teams actually deploying AI. For most coding and knowledge-work workflows, the right question isn't which model is the most powerful, but which one delivers reliable results at a sustainable cost and speed. Having it available on Google Cloud also makes it easier for companies already on that platform to try it without rebuilding their infrastructure. Curious to see how teams measure the real-world savings

A strong step forward for practical AI adoption. The focus on faster, more capable delivery is especially relevant for CIOs balancing innovation with responsible governance. Clear value will come from pairing these advances with sound operating discipline.

Interesting

Lower cost per task and faster execution could make advanced AI much more practical for everyday development work.

Interesting to see the emphasis on lower cost per task rather than just raw capability. For founders and teams here, how are you choosing between models today: benchmark scores, cost per task, speed, or how well they fit your existing cloud setup? Would love to hear what is driving your decisions in practice

The lower cost per task is what really stands out. Making powerful AI more affordable could speed up adoption across everyday development workflows.

That's a great inclusion done by Google Cloud for providing Claude Sonnet 5.5 on their platform. This will be a great help for coders and developers as it is completely focused on coding and they also have a great chance to share their knowledge and work.