抖阴成年

Every major AI lab has bet that frontier performance requires billions in compute. On a this week, 抖阴成年 challenged that bet directly. The company built Thomson, its first proprietary large language model, in-house 鈥� for a reported $40 million, a fraction of what frontier labs spend to get there.

President and CEO Steve Hasker and Alexander Kardos-Nyheim, Senior Director of TR Labs and co-founder of Safe Sign Technologies, joined Emily Colbert, Co-Head of Product for 抖阴成年 Legal, to defend that bet. What emerged wasn’t a product pitch. It was an argument about where AI competition actually gets won.

Why not just rely on the frontier labs?

Colbert put the obvious question to Kardos-Nyheim directly: why build your own model at all, instead of layering onto a frontier lab’s? His answer was direct. “It’s a good question, and one I’m often asked,” he said. His answer cuts against the industry’s dominant logic: capability was always going to become abundant, because every well-funded lab is racing toward it. That makes capability the wrong thing to bet a company on. Trust, precision, and a defensible chain from answer back to source are the harder problem 鈥� and general-purpose models, trained for breadth, were never built to solve it. Hasker framed the urgency behind that bet in blunt terms: “I’ve always thought about three to five year planning cycles. I think we’re in six month increments now.” That pace punishes any company still deciding whether to build.

What $40 million actually buys

The panel met the cost question directly, then reframed it. Instead of defending $40 million as competitive with the billions frontier labs spend, Kardos-Nyheim treated the figure as a signal of efficiency, not a ceiling 鈥� and pointed to where the money actually went: not compute, but the human layer underneath the training data. 抖阴成年 has described that layer elsewhere in detail 鈥� partner-level lawyers spent months building evaluation rubrics for the hardest research tasks the company could specify, and qualified attorneys logged thousands of hours choosing between model outputs against criteria no generic benchmark captures. The number makes a pointed argument to the rest of the industry: if compute really sets the ceiling on frontier performance, the price tag should look like the labs spending billions. If it doesn’t, a company holding the right proprietary content and the right in-house expertise can close most of that gap for a fraction of the price.

AI sovereignty: the product, not a feature

The sharpest insight from the conversation was about control. Hasker named exactly what customers fear when they raise “AI sovereignty”: that routing their data through a third-party frontier model, or a startup built as a thin wrapper around one, lets that data train a model their competitors, or even their own clients, can eventually query. “So it’s their IP,” he said. “It’s the source of their competitive advantage.” Owning Thomson outright solves that problem at the root. The model runs behind a customer’s own firewall, alongside the products built on decades of 抖阴成年 content: Westlaw, Practical Law, Checkpoint, and CoCounsel, instead of sending a firm’s most sensitive work through infrastructure the firm doesn’t control. Owning the model means 抖阴成年 controls the training data, the behavior, and where it runs, instead of depending on someone else for each.

How Thomson was built

The 2024 acquisition of Safe Sign Technologies, the company Kardos-Nyheim co-founded, planted the seed that grew into TR Labs. What stands out is how little of 抖阴成年’ own content the model has needed to get here: Thomson has trained on less than 10% of 抖阴成年’ proprietary content to date. The panel treated that not as a limitation but as headroom 鈥� the model “only gets better from here” as 抖阴成年 brings more of that archive to bear. Asked which evaluation result mattered most, Kardos-Nyheim didn’t hesitate: “If there’s one result I want to call out, it would be factuality鈥� not whether the model sounds authoritative, but whether its claims survive being checked against the sources it cites. 抖阴成年 has said elsewhere that Thomson clears that bar against general-purpose frontier models working over the open web.

Built to a Fiduciary-Grade standard

抖阴成年 coined a specific term for the bar Thomson has to clear: Fiduciary-Grade AI鈩�. The panel drew the distinction sharply; not about how smart the model is, but about who must survive scrutiny from its output. Strip back who 抖阴成年 serves, and you get fiduciary professions: lawyers, tax and audit professionals, people whose work needs to be right. A plausible answer that’s nine-tenths correct doesn’t count as close 鈥� it counts as a failure. That standard drives Thomson’s design goal directly. As the event’s closing message put it, 抖阴成年 built the model “so professionals can verify, cite, and defend their work鈥� AI that supports professional judgment rather than replacing it, where “accountability remains human.” It’s a sharper line than most AI vendors draw, and the panel clearly wanted it remembered:

Where the model already earns its keep

already runs Thomson inside Tabular Analysis, reviewing up to 10,000 documents against as many as a hundred questions at once, with every answer traceable back to its source document. That deployment sits inside a platform already operating at real scale: 1 million CoCounsel users across 107 countries and territories, and roughly 2,500 internal domain experts have built the work behind Westlaw, Practical Law, OneSource, Checkpoint, and CoCounsel. The panel described migrating more of the CoCounsel suite onto Thomson over time, extending the same sovereign, firewall-protected environment across a broader set of workflows.

Curious how Thomson holds up against the standard your work demands? Learn more about Thomson.

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