Power Points: Thomson Reuters has launched Thomson, its first proprietary large language model, trained using its vast stores of legal, tax and professional information. The significance is not simply that another AI model has arrived. Thomson Reuters is attempting to turn the Westlaw and Practical Law data moat into an AI moat, reducing its dependence on OpenAI, Anthropic and other foundation-model providers while challenging the increasingly crowded legal AI market.
Thomson Reuters has formally entered the frontier-model business with Thomson, a proprietary large language model built specifically for professional work. This may be a definative move from Australian-born TR CEO Steve Hasker, (above) using his private banking and McKinsey smarts to bloody the noses of the the multitude of legal AI tools confounding and confusing the legal market.
And unlike the usual AI launch involving several billion dollars, an aircraft hangar full of GPUs and extravagant predictions about humanity’s imminent transformation, Thomson Reuters says it spent around $40 million training the model.
The company says Thomson was developed from a strong open-source foundation and then specialised using decades of proprietary content and expertise drawn from Westlaw, Practical Law, Checkpoint and Reuters.
What is also intriguing is that Thomson Reuters says it has so far trained the model using less than 10 percent of its proprietary content. So the move is aggressive, but also opens up the Thomson Reuters legal motherlode. One day.
The company is positioning Thomson as what it calls “Fiduciary-Grade AI”, meaning AI designed for professions where answers need to be verifiable, auditable and rather more dependable than the confidently invented case citation that has already landed more than a few lawyers before irritated judges.
The Numbers Are Interesting
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