A practical session on cutting AI costs without sacrificing quality. The speakers argue for measuring cost per completed task rather than per-token list prices, show how to right-size models with accuracy-versus-cost Pareto analysis, and walk through four levers: prompt caching, programmatic tool calling, reasoning effort, and batch or flex processing. Customer case studies from Perplexity, Notion, Clio, and Blitzy ground the advice.
Meta's Nishant Gupta and Naman Ahuja explain how AI inference, the company's fastest-growing workload, requires a dedicated orchestration layer akin to Kubernetes for virtual machines. They detail the unique challenges of LLM serving compared to traditional microservices, emphasizing the intricate coupling of infrastructure layers, the distributed transaction nature of requests, and a seven-axis framework for scheduling. The talk concludes with the imperative to optimize for "cost per successful task" and the necessity of an inference-specific control plane to manage the complex interplay of latency, cost, and throughput.
Independent model evaluation is crucial for AI progress, as public benchmarks are saturated and models optimize for them. Rayan Krishnan discusses VALS's role in providing continuously evolving, third-party evaluations, highlighting the Llama 4 'disaster' and the need to measure increasingly agentic systems. The conversation covers the complexities of evaluation beyond capability to include cost and latency, the looming financial impact of token spend on enterprises, and the critical role of evaluations in informing AI policy and international coordination for aligning AI with human intent and managing geopolitical risks, especially concerning recursive self-improvement.
DoorDash's GenAI platform team transformed evaluation from an engineering-centric task into a cross-functional workflow. By adopting an API-first strategy, they empowered non-engineers like Strategy & Operations to "vibe code" their own annotation UIs using coding agents and self-serve calibrate LLM judge prompts. This approach dramatically reduced annotation costs, accelerated iteration, and fostered broader organizational ownership of AI quality.
LLM leaderboard scores often don't reflect real-world performance. This video explains why and outlines a comprehensive approach to evaluate AI systems, focusing on the critical balance of accuracy, latency, and cost. It details model and system evaluation techniques, including handling different inference phases, workload shapes, and specific considerations for AI agents, emphasizing the need for realistic testing over generic benchmarks.
Giedrius Šteimantas discusses the often-overlooked infrastructural layer for AI agents operating on the open web. He highlights how traditional browser automation leads to high costs and unreliability, particularly due to CAPTCHAs and invalid content. Drawing from ten years of web scraping experience at Oxylabs, he presents a multi-stage solution that prioritizes cost-efficiency and content validation, using specialized APIs and hardened headless browsers only when absolutely necessary, thus significantly reducing token waste and improving agent performance.