AI Agents Can Only Pick Two - Fast, Cheap, or Smart

Explore the critical trilemma facing AI engineers in this 43-minute interview with Meryem Arik, Co-Founder of Doubleword, examining why AI agents can only optimize for two out of three key factors: speed, cost, or intelligence. Discover how inference compute has overtaken training compute for the first time and learn why most AI engineers aren't approaching this shift strategically. Delve into Doubleword's contrarian approach of prioritizing cost-effectiveness and intelligence over low latency, and understand how Meryem identified the inference opportunity before ChatGPT's launch. Examine the economics behind the prediction that 66% of all AI compute in 2026 will be dedicated to inference, and learn about the three drivers of exponential token generation growth. Master batch inference concepts, including why it offers 50%+ cost savings and how async agents are transforming inference economics. Analyze real-world applications including dataset generation, multi-bot routing, and large-scale data processing, while exploring Doubleword's four-stage model of enterprise AI adoption. Investigate the technical challenges of deploying trillion-parameter models and understand how agent-spawned sub-agents will drive future token demand. Gain insights into practical strategies for AI engineers currently using expensive real-time APIs for non-time-sensitive tasks and discover cost-effective scaling approaches for AI founders.