Thomson Reuters

Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI

Jul 31, 2026 · 19:05

DatologyAI CEO Ari Morcos argues data quality is the compute multiplier: better data steepens scaling, so the same compute buys better models. His oil-refinery approach—clean, curate, create, compose—uses synthetic rephrasing for diversity; curation let a VLM beat the public Pareto frontier with 145x less training compute and match Qwen 3.5 with 35x fewer flops per correct answer. Curating English also boosts non-English via cross-lingual transfer. For Thomson Reuters, mid-training on curated legal data lifted LegalBench 5 points without catastrophic forgetting and tripled post-training gains. Arcee's Trinity Large, trained on 17 trillion curated tokens, matched GLM-5 and Kimi and beat Claude on some tasks for under $20 million, proving data curation is cheaper than compute.

From Copilot to Colleague: Trustworthy Agents for High-Stakes - Joel Hron, CTO Thomson Reuters

Jul 23, 2025 · 19:45

Thomson Reuters CTO Joel Hron explains the shift from helpful AI assistants to productive, agentic systems in high-stakes legal, tax, and compliance workflows. He frames agency as a spectrum with dials for autonomy, context, memory, and coordination, which must be tuned to the risk tolerance of professional users. Key lessons include building the whole system first rather than over-indexing on minimal MVPs, treating legacy applications as assets to be decomposed into tools for agents, and the persistent challenge of evaluating agentic systems with expensive, variable human expert judgments. Hron demonstrates a tax product that extracts data from documents and maps it to a calculation engine, and a legal research agent that uses proprietary case law tools to produce cited reports. He emphasizes leveraging unique organizational assets—like Thomson Reuters' 4,500 domain experts and terabytes of proprietary content—to create differentiation.

The missing pieces of workflow automation — Shirsha Chaudhuri, Thomson Reuters Labs

Apr 23, 2025 · 14:37

Shirsha Chaudhuri, head of co-innovation at Thomson Reuters Labs, identifies eight missing pieces preventing true AI workflow automation in the enterprise. She argues that while 71% of Fortune 500 companies still run mainframes and 68% of IT production workloads remain on mainframe, current agentic efforts lack connectors to bridge legacy systems, standardized agent architectures, and reliable ROI metrics. She highlights the need for domain experts to reimagine processes alongside AI practitioners, collaborative UX design, and AI governance that translates ethics into agent architecture. Control balance between deterministic and agent-driven steps remains unresolved, and the fast-evolving agent lifecycle lacks a clear update strategy. Drawing from Thomson Reuters' own journey—from a 2023 open AI arena through RAG and prompt engineering to 2024's agentic experiments—she calls for a reimagined workflow design rather than just task-level automation.