For three years, artificial intelligence infrastructure has been dominated by one idea: Bigger is better. Bigger models, bigger clusters, and bigger checks written to a small group of suppliers. At this week’s Open Compute Project Global Summit, Intel Corp. argued that the next phase of AI will look very different and that Intel is well-positioned to benefit.
Every information technology leader I talk to has an artificial intelligence story about customer experience. Almost none has an outcome story. That gap is now measurable. Talkdesk Inc., in partnership with NewtonX, surveyed 252 director-level-and-above leaders in customer experience, IT, operations and AI strategy at midmarket and enterprise organizations across four regions.
Per-token pricing was the best thing to happen to enterprises looking to experiment with artificial intelligence, but it may be the worst thing for AI in production. That’s the quandary at the center of a new Futurum report, “The Off Ramp From Per-Token Pricing,” sponsored by neocloud provider QumulusAI Inc. The report’s key finding is that agentic AI can drive token consumption per task 10 to 100 times higher than a simple inference call. Agentic AI being more expensive is likely no surprise, but it’s good to see the report quantify it.
For decades, the observability industry has answered one basic question: Is it running? Agentic artificial intelligence breaks that model. An agent can return a clean response, meet its latency target and throw no errors, yet still give a customer the wrong answer, call the wrong tool or pull from a stale knowledge base. By every traditional metric, the system is healthy, but the only metric that matters to the business is that it failed.
Every organization I talk to wants to be “AI-ready,” but few want to discuss what that entails. It’s rarely a new model or agent. More often, it’s the unglamorous work of ripping out a decade’s worth of infrastructure debt so the network can deliver the bandwidth, consistency and data that artificial intelligence depends on.