Otech Talks : Oracle Technology Worth Talking| Blogging |lifestyle

Over the last couple of years, most of my conversations around AI have been about capability. How fast models are improving. How agents are becoming more autonomous. How enterprises can adopt GenAI safely. How teams can redesign workflows around intelligence. But this week, I found myself thinking about something deeper. Not what AI can do. But what does AI cost? And I don't just mean money. I mean energy. I mean infrastructure. I mean the hidden assumptions underneath the current AI boom. Because when we talk about the future of AI, most people immediately jump to models, chips, data centers, agents, and software stacks. But as someone who works closely with organizations trying to operationalize AI in the real world, I keep coming back to a harder question: What happens when the current compute model itself becomes the bottleneck? This is not a question most teams are asking yet. But it is a question serious builders should start paying attention to. This week, while reviewing different enterprise AI patter