Why did Thomson Reuters build its own model?
“We built Thomson first and foremost for ourselves. We wanted more control over the intelligence inside our products: to make them better, improve the economics, reduce our dependence on someone else’s roadmap and give ourselves greater sovereignty over how that technology is trained and deployed. As access to powerful models becomes increasingly commoditised, we think owning more of that intelligence becomes strategically important.”
Why build on an open-weight model rather than continue relying entirely on frontier providers?
“Historically there has been a trade-off. You could use the most capable closed models and accept the dependency that comes with them, or you could take greater control with an open model and accept some distance from the frontier. What Thomson demonstrates is that those things do not have to be mutually exclusive. We can have control and sovereignty while still building a model that competes at a very high level.”
What makes Thomson difficult for someone else to replicate?
“The open-source foundation is the starting point, not the moat. The difficult part is everything that happens after that: the data, the training methodology, the domain expertise, the preference data and the evaluation infrastructure required to know whether the model is actually getting better at professional work. Thomson Reuters has spent decades building the corpus and expertise behind that system, and our research team has spent years working on how to translate those assets into model performance.”
Why did you only use about 10% of Thomson Reuters content?
“Less than 10% so far. And what comes next is not simply feeding the model more data. It is identifying which content will actually improve performance, turning that content into high-quality training data, and validating the results with the same expert-led process we have used to date. The fact that we have reached this level of performance using a relatively small portion of the content available to us gives us a lot of runway to keep improving Thomson across more tasks and workflows.”
What does Thomson mean for CoCounsel and Thomson Reuters products?
“Thomson gives us another form of intelligence that we control and can optimise around the work our customers actually do. We can use Thomson where it is the best model for the task, use third-party models where they are better, and keep evaluating that mix as the technology changes. The goal is not to force every problem through our model. It is to give CoCounsel the ability to use the right intelligence for the right work while improving the economics and control behind the system.”
Does Thomson mean customers have to choose between capability and sovereignty?
“That is one of the most important things we think Thomson demonstrates. A law firm or another professional organisation should not necessarily have to choose between the capability of a frontier model and the security, control and sovereignty advantages of owning more of its AI stack. We have shown there is a path to both. That creates a very different set of choices for organisations thinking seriously about their long-term AI strategy.”
Could law firms or other organisations eventually license Thomson directly?
“Yes. We built Thomson to make our own products better, but in doing that we have created a lot of optionality. There are law firms, corporate legal departments, development teams and other organisations that want to take more direct ownership of their AI strategy. They may want greater control over deployment, more sovereignty over their technology stack or the ability to build directly on a model designed for professional work. We are very open to working with those organisations and to Thomson being licensed directly.”
How big could that opportunity become?
“We built Thomson as infrastructure for Thomson Reuters. What we are beginning to see is that it could also become infrastructure for others. A lot of organisations today are deciding which models are best for which work. Thomson gives us the opportunity not only to be the company building applications on top of AI, but potentially to provide some of the underlying intelligence as well for companies making those model choices. That was not the primary reason we started the project, but it is a very interesting opportunity that comes from what the team has built.”