Thomson 1: The New $40M Legal AI Model | Thomson Reuters Joel Hron
Joel Hron, CTO of Thomson Reuters, breaks down how the 175-year-old company trained its own legal AI model, Thomson, for around $40 million by stacking proprietary content and expert judgment on open-source foundations. He explains why owning the model layer beats renting frontier intelligence, and how a rebuilt CoCounsel jumped from 25% to over 70% accuracy.
Behind Thomson, the new legal AI model from Thomson Reuters, is a $40 million investment in people, compute, and evaluation methods. The final training run cost just $450,000. CTO Joel Hron, whose teams build Westlaw, Practical Law, and CoCounsel for millions of professionals in more than 100 countries, joined us for the launch to break down why the 175-year-old company chose to own its model layer instead of solely renting frontier intelligence.
We cover:
- Why Thomson Reuters trained its own model instead of relying only on Claude, GPT, or Gemini
- The compute, data, and expertise flywheel behind the Thomson model
- How rebuilding CoCounsel around agent-native tools took one-shot accuracy from 25% to over 70%
- The rent-versus-buy case for owning model weights and compounding expert feedback over time
- The dangers of AI inaccuracies in legal work
- Citation ledgers, deep research, and verifying legal work with no ground-truth oracle
- Joel's advice to CTOs weighing open models and training on their own data
Chapters:
(0:00) Cold open: a $40M model and 25% to 70%
(0:27) Why Thomson Reuters built the Thomson model
(2:46) From information services to an AI company
(5:14) The flywheel: compute, data, and expertise
(8:21) The oldest company to ship a model?
(9:47) Training for users without catastrophic forgetting
(14:37) Continuous pre-training on Westlaw and Checkpoint
(15:29) Fine-tuning, DPO, and agentic reinforcement learning
(17:37) Rebuilding CoCounsel: 25% to 70% overnight
(21:48) Capturing expert judgment: own versus rent the model
(28:59) Managing lawyer time and protecting customer IP
(31:45) Eval results and avoiding catastrophic forgetting
(34:34) Tabular analysis and legal deep research
(36:26) Benchmarks, Harvey, and frontier comparisons
(39:26) Verifying legal work with no ground-truth oracle
(41:54) Citation ledgers and the hallucinations that matter
(45:41) Rebuilding the platform and the Trust in AI Alliance
(48:59) Advice to CTOs on open models and owning intelligence
(51:31) The compounding flywheel and what comes next
(0:27) Why Thomson Reuters built the Thomson model
(2:46) From information services to an AI company
(5:14) The flywheel: compute, data, and expertise
(8:21) The oldest company to ship a model?
(9:47) Training for users without catastrophic forgetting
(14:37) Continuous pre-training on Westlaw and Checkpoint
(15:29) Fine-tuning, DPO, and agentic reinforcement learning
(17:37) Rebuilding CoCounsel: 25% to 70% overnight
(21:48) Capturing expert judgment: own versus rent the model
(28:59) Managing lawyer time and protecting customer IP
(31:45) Eval results and avoiding catastrophic forgetting
(34:34) Tabular analysis and legal deep research
(36:26) Benchmarks, Harvey, and frontier comparisons
(39:26) Verifying legal work with no ground-truth oracle
(41:54) Citation ledgers and the hallucinations that matter
(45:41) Rebuilding the platform and the Trust in AI Alliance
(48:59) Advice to CTOs on open models and owning intelligence
(51:31) The compounding flywheel and what comes next
Connect with Joel Hron:
- LinkedIn: https://www.linkedin.com/in/joel-hron-90a3421a/
- Thomson Reuters AI: https://www.thomsonreuters.com/en/artificial-intelligence
- The Thomson model and next-gen CoCounsel Legal: https://www.thomsonreuters.com/en/press-releases/2026/august/thomson-reuters-launches-next-generation-of-cocounsel-legal-the-ai-ecosystem-built-for-legal-professionals
Connect with Chain of Thought host Conor Bronsdon:
- Newsletter: https://newsletter.chainofthought.show/
- Twitter/X: https://x.com/ConorBronsdon
- LinkedIn: https://www.linkedin.com/in/conorbronsdon/
- YouTube: https://www.youtube.com/@ConorBronsdon
Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000.
Thanks to Walrus Memory, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot.
Creators and Guests
Guest
Joel Hron
Joel Hron is the chief technology officer of Thomson Reuters, where his teams build Westlaw, Practical Law, and CoCounsel for professionals in more than 100 countries. He joined Chain of Thought at the launch of Thomson, the company’s own legal AI model, to explain why a 175-year-old company chose to train its own model for about $40 million rather than only rent frontier intelligence, and how rebuilding CoCounsel around agent-native tools moved one-shot accuracy from 25% to over 70%.
