Bigger Isn't Better: The Case For Rightsized AI

InnovationBigger Isn't Better: The Case For Rightsized AIByIri Trashanki,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 18, 2026, 09:45am EDTIri Trashanski, Chief Strategy Officer at Ceva, is shaping the future of the Smart Edge with extensive experience across tech sectors. getty​The conversation around artificial intelligence is still largely centered on AI compute. Faster processors, larger models and more powerful infrastructure dominate the headlines. But as AI moves from the cloud into real-world environments, a different challenge is starting to take shape at the edge. As processing speed is critical, delivering the rightsized, purpose-built inference engines that fit the constraints of real-world systems and the specific needs of each application will enable the best latency per use case and device type.We are entering a phase where AI must operate not just in data centers, but across billions of devices spanning cameras, sensors, vehicles, industrial systems and consumer electronics. These systems must connect, sense and interpret their environment and make decisions locally. That shift fundamentally changes what it takes to make AI work.Two Worlds Of AI EmergingToday, AI is split into two distinct domains.The first is the data center. This is the world of hyperscale and neocloud infrastructure, where performance is measured in throughput and scale, and where power and cost constraints are very different. Architectures in this environment are optimized for training large models and pre-filling and decoding multitrillion parameter models.The second is the edge, where AI interacts with the physical world. Devices must operate under tight constraints of limited power, AI compute cycles, real-time responsiveness and strict cost requirements. They must process data from multiple sources, often in unpredicta...