Model orchestration

Oct 08, 2026

$450K TO TRAIN A FRONTIER AI MODEL?

Thomson Reuters CTO Joel Hron explains why the company built Thomson, a domain-specific frontier model adapted from open-weight checkpoints like Qwen 3.5, using decades of proprietary legal and tax content and expert judgment. The conversation covers the training lifecycle from realignment and continual pre-training to agentic RL, the economics ($450K final run versus roughly $40M total), team structure, CoCounsel orchestration, verification challenges in legal and tax, and the future balance between open-weight and frontier models.

Oct 06, 2026

The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI

Varun Krovvidi of Resolve AI explains why AI excels at code generation but struggles with the complex, multi-domain problem of running and fixing software in production. He outlines five common failure modes for AI agents in production (anchoring bias, context issues, coherent vs. causal answers, guardrails, lack of learning) and introduces Resolve AI's six-pillar architecture: model orchestration, context engineering, causal reasoning, governed actions, learning systems, and multi-level evaluations. A demo illustrates how Resolve AI diagnoses a Grafana alert to its root cause, ruling out coincidental events and maintaining its causal reasoning.

Sep 04, 2026

Open Models Change The Economics of AI

Ollama Co-founder Jeffrey Morgan discusses the enterprise shift to open models, driven by cost, customization, and coding agents, leading to a 150X token growth. He highlights the dominance of Chinese models in cloud consumption, the hardware renaissance enabling local LLMs, and the rise of ultra-efficient "flash" models. Morgan also shares Ollama's journey, from its YC pivot to its monetization strategy, and reflects on lessons for building AI companies.

Aug 22, 2026

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

This talk argues against the common practice of picking LLMs based solely on leaderboards, emphasizing that there's no single best model, only the right one for a given request. It introduces Digital Ocean's Inference Router, a customizable, open-source solution that intelligently selects models based on user-defined preferences (cost, latency, task, quality) rather than benchmarks, demonstrating significant cost savings and performance improvements in live demos.

Aug 21, 2026

Stripe buys OpenRouter, Ramp’s AI Index & IBM’s OpenAI deal

This episode explores the dynamic landscape of the AI industry, dissecting IBM's strategic alliance with OpenAI for enterprise AI integration, Stripe's acquisition of OpenRouter to capitalize on AI token routing, and insights from Ramp's AI Index revealing evolving AI spend and the rise of open-source models. It highlights the shift from model-centric to infrastructure and governance-centric AI, and touches on the ethical implications of AI-drafted legislation.

May 24, 2026

Scaling the Next Paradigm of Heterogeneous Intelligence — Adrian Bertagnoli, Callosum

Adrian Bertagnoli from Callosum argues that the era of scaling monolithic models on homogeneous GPU clusters is ending. He introduces "heterogeneous intelligence," a new paradigm where model architectures, chip types, and workflows are optimized together. By routing subtasks to the most efficient model and hardware, this approach achieves significant performance gains, as demonstrated by two key results: a 7x cost reduction in recursive reasoning tasks using Cerebras, and state-of-the-art performance on the Video Web Arena benchmark, outperforming leading GPT and Gemini models at a fraction of the cost and time.