I don't trust that they'll be able to make back that $40M.
This feels very much like a news agency getting into crypto or launching its own NFT line.
Or IBM selling Watson.
Or Mozilla chasing every which thing.
They're not stakeholders in the future of work. They're just wanting to stay relevant and pattern matching against what they see.
Reuters is too important for this.
If they were trying to use this as a narrative affront to OpenAI and Anthropic, maybe, but this is Reuters, not a deeply political organization seeking to land gotchas against big tech.
No it's just a value add to their existing data products and a moat against the big n LLM companies. The "news" part of the business is relatively small compared to everything else the do.
Like how Bloomberg does news but its far from their only or primary product. TR covers a different surface of data products than Bloomberg but it's a decent comparison.
1. Marketing and expressing to their customers that they are not falling behind, and
2. Insulating themselves from frontier labs jacking up prices, nerfing the models they depend on, or otherwise unexpected changes in behavior.
I think the main goal is #2. Thomson Reuters might be a $40B company, but.... at this point it's not clear that that holds any weight in terms of not being fucked over by 2 companies aiming for $2t+ IPO valuations.
Edit: On second thought, there is probably a #3 too. They can serve inference for their own models significantly cheaper than frontier lab rates (assuming they're capturing continuous use of their hardware). I still think #2 is the primary goal.
> I think the main goal is #2. Thomson Reuters might be a $40B company, but.... at this point it's not clear that that holds any weight in terms of not being fucked over by 2 companies aiming for $2t+ IPO valuations.
It's exactly the same sorta thinking re; Microsoft potentially fucking over the PC videogames industry that Valve used to justify the zillions of dollars and countless man-hours put into their big push for Linux gaming rather than tie themselves to a single proprietary company that could try to kick them out of the gaming industry. So far it's going pretty well for them. Depending on how they play their cards, this could also work out really well for Thomson Reuters as well.
I know you're being facetious, but I genuinely think Thomson Reuters should be investing in NFTs. NFTs (while some of the shine has admittedly worn off) are an emerging infrastructure for digitally native ownership, and that's precisely the sort of institutional problem Thomson Reuters is positioned to solve (think tax records, medical records, etc).
People in tech suddenly tossing NFTs to the side because AI came along makes no sense to me. HN should be as bullish on NFTs now as it was in 2021.
This is going to increasingly happen over the years to come. Big organizations will become more sophisticated with operationalizing their data, training and running LLMs will continue to be demystified and accessible, and over time we'll get more and more specialized / industry-specific models.
It's going to become another way to monetize your informational assets if you're a big older enterprise with troves of data. All you need is time to figure out how to make it useful for yourself and then eventually sell access to it however you want.
Think of all the data that big orgs have that isn't accessible to all the AI labs to suck up.
They're trying to find a moat in the AI era, for their relatively gigantic news business (and they're among the few still standing giants in news). Most of these organizations are very scared of what AI might do to them.
I'm pretty sure, given the reference to fiduciary grade standards that they are doing more to secure their moat in Legal, Accounting and related fields.
Reuters News is only slightly over 11% of their business. Legal and Accounting is closer to half.
on edit: just went and looked it up, adding in compliance offerings it is over 80% of their business.
Because "bigger = better" does not work for highly specialized expertise, where the knowledge is not available on the public Web. It is naive to believe it's all open out there merely because the Web is large and we have Wikipedia.
If you are a highly specialized professional intellectual property paralegal, a forensic tax investigator or a post-market pharmacovigilance analyst, you will need for-profit knowledge sources, and your answers will often require synthesizing and interpreting multiple sources.
China is fucking smart. They have technical expertise to know that by feeding the model certain data, they can shape the outcome of questions posed.
Yeah, in the end, maybe both US and Chinese models can solve Fizz Buzz, or some Erdős problem.
And they can answer our inquisitive minds when we wonder, "Why is the sky blue?".
But deep questions about what is normal in the world they can change the outcome of.
And "Why is the sky blue?" is a deeper question than we think. Because it isn't blue everywhere in the world right now. And whether it is the fault of the US automotive industry, or aggressive investment by China in their own manufacturing base matters.
Of course, both have been responsible for pollution at different times. Growing up in Michigan near Dow Chemical I know this.
But depending on how they select training data answers can be nudged one way or another.
The US is smart too, and they have people working on the same things. It's ironically easier for me to talk about how China's security services are likely to shape their models' perception of the world. Which is a damn shame, because we deserve unbiased answers so we can all help our country improve.
Or countries improve if you want to take a global shared world view.
> Our evaluation found Thomson’s citation quality generally competitive with leading frontier models, even when tested on Canadian employment-law questions without a Canada-specific setting.
That’s it? It was generally competitive with leading frontier models? Neat, but why would someone pay for frontier models and also a generally competitive additional product?
> Thomson Reuters is also making a “small” version of Thomson available as an open-weight model on Hugging Face for academic and non-commercial use to further aid in this validation.
Pretty cool someone is still doing this. Training in house LLMs was extremely popular in 2023-2024, back when domain-specific LLMs could easily top GPT in their field. In my field alone (tax/HR tech) I remember that Intuit, Workday, Indeed, LinkedIn were all training internal models.
It eventually stopped making sense because of inference costs. Running something internal with 30% GPU utilization is just too cost inefficient compared to using an API. Idk how Reuters will manage to solve this fundamental problem.
Yep, this is the fundamental issue. It's a 35BA3B model and they probably finetuned it and evalled it in one bursty week on an 8xH100 rental just fine. But long term inference is always going to be easier in an API.
I don't see why you couldn't see improvements in self-hosted or hosting-as-a-service model throughput? Basically API-style support for a company's internal LLM system. Why not? Or secure infra offered by AWS to self-host your own models that get served to the company just like any other company internal service can be hosted on AWS or similar?
It won't match Anthropic or OpenAI, but it could be economic?
It's possible, but it depends a lot on the nature of the data sovereignty guarantees. Today for many companies, the kind of guarantees they have with AWS amounts to basically legal coverage. And that is fine when the company is itself protecting data only due to legal or regulatory reasons. But if the company is protecting it for commercial reasons... then a different model is needed. It could happen, but it has to be worked out.
> Running something internal with 30% GPU utilization is just too cost inefficient compared to using an API. Idk how Reuters will manage to solve this fundamental problem.
That's actually easy, because you can solve it through doing nothing and simply declaring that optimizing for lowest cost is not the main goal.
The fundamental-ness of that problem is entirely man-made and thus can easily be declared void as long as you have the cash to back that up.
Which might be a winning strategy in a world where everyone else is not doing that. Plus that your knowledge stays in-house, etc.
What makes sense also depends on one's business model: TRI charges premium dollars for access to their systems, so there is no need to optimize for cost; trust in the answers is the currency of knowledge workers in today's complex domains.
At the Thomson Reuters family of companies (technically then: Refinitiv Ltd. sold to LSEG), the first foundational model (in the sense of "trained entirely from scratch") was trained already in 2018 (i.e., pre-ChatGPT); it would even have been earlier, but the electricity wires and fuses in the rented 5 Canada Sq, Canary Wharf office had to be replaced first at the time to deal with the current needed to serve the GPUs.
It’s based on qwen, not fully trained internally. I expect we will way more of this in the future, it’s pretty cheap to fine tune an open weight model for your specialized niche
Cool that they did this on top of Qwen3.6-35B-A3B. If they have their own collection of valuable data this is the only way to make sure it doesn’t end up in general purpose models. That’s probably enough justification for the $40m spend - continued control of your destiny as an information provider.
Afaik... Lord Jacob Rothschild is 30% owner of Woodbridge, which holds Reuters. I believe the Thomson family owns the rest... The richest family in Canada. Woodbridge owns large shares of textbook companies, radio stations, news services, wires, scientific journals past and present.
> Frontier labs have typically spent billions of dollars on compute and years of infrastructure investment to reach the frontier. Thomson Reuters took a different path: starting from a strong open-source foundation and investing $40 million to train Thomson into the right intelligence for the jobs that matter most, covering talent and compute
Weird flex to boast that fine-tuning is cheaper than a full pre/mid/post-training run.
Someone else had to spend the billions to make and release the open model. That doesn't make your "different path" a breakthrough in efficiency and cost savings. Also you're not a frontier lab.
Please challenge me and explain why this is a break through worth the headline they use for their own work.
How I understand it, without really reading into it:
- Thomson Reuters did not "create" a frontier model, they gave some money, maybe a bit of their data to Imperial College London, and took Alibaba's Qwen 3.6 35B A3B Model for a basic fine tune.
- "They" (some undergrads at Imperial College London) used an existing ablation framework to undo some of the topic alignment of the original model.
- "They" fine tuned on some domain knowledge trying to preserve general knowledge - here maybe, just maybe some data came from Thomson Reuters.
As a result: one of 100's of Qwen 3.6 35B A3B sparse model fine tunes, just for publicity, to write overstating headlines like "X created their own frontier model"
That's pretty wild that a Ctrl-F of "qwen" or "china" or "alibaba" on the thomson reuters announcement URL turns up no results. They want to portray it like they developed this thing from scratch, when that is by no means what actually happened.
They didn’t say they created it no? I don’t think any of those points matter. It’s a business announcing a new product. Why does it matter if they built it internally or used external teams? I find strange to focus on those details
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[ 0.25 ms ] story [ 16.2 ms ] threadhttps://huggingface.co/thomsonreuters/Thomson-1.0-Small
This feels very much like a news agency getting into crypto or launching its own NFT line.
Or IBM selling Watson.
Or Mozilla chasing every which thing.
They're not stakeholders in the future of work. They're just wanting to stay relevant and pattern matching against what they see.
Reuters is too important for this.
If they were trying to use this as a narrative affront to OpenAI and Anthropic, maybe, but this is Reuters, not a deeply political organization seeking to land gotchas against big tech.
Like how Bloomberg does news but its far from their only or primary product. TR covers a different surface of data products than Bloomberg but it's a decent comparison.
It's likely split between two goals:
1. Marketing and expressing to their customers that they are not falling behind, and
2. Insulating themselves from frontier labs jacking up prices, nerfing the models they depend on, or otherwise unexpected changes in behavior.
I think the main goal is #2. Thomson Reuters might be a $40B company, but.... at this point it's not clear that that holds any weight in terms of not being fucked over by 2 companies aiming for $2t+ IPO valuations.
Edit: On second thought, there is probably a #3 too. They can serve inference for their own models significantly cheaper than frontier lab rates (assuming they're capturing continuous use of their hardware). I still think #2 is the primary goal.
It's exactly the same sorta thinking re; Microsoft potentially fucking over the PC videogames industry that Valve used to justify the zillions of dollars and countless man-hours put into their big push for Linux gaming rather than tie themselves to a single proprietary company that could try to kick them out of the gaming industry. So far it's going pretty well for them. Depending on how they play their cards, this could also work out really well for Thomson Reuters as well.
People in tech suddenly tossing NFTs to the side because AI came along makes no sense to me. HN should be as bullish on NFTs now as it was in 2021.
I also doubt that R&D spending having to "make back" directly is a winning strategy.
It's going to become another way to monetize your informational assets if you're a big older enterprise with troves of data. All you need is time to figure out how to make it useful for yourself and then eventually sell access to it however you want.
Think of all the data that big orgs have that isn't accessible to all the AI labs to suck up.
Reuters News is only slightly over 11% of their business. Legal and Accounting is closer to half.
on edit: just went and looked it up, adding in compliance offerings it is over 80% of their business.
If you are a highly specialized professional intellectual property paralegal, a forensic tax investigator or a post-market pharmacovigilance analyst, you will need for-profit knowledge sources, and your answers will often require synthesizing and interpreting multiple sources.
China is fucking smart. They have technical expertise to know that by feeding the model certain data, they can shape the outcome of questions posed.
Yeah, in the end, maybe both US and Chinese models can solve Fizz Buzz, or some Erdős problem.
And they can answer our inquisitive minds when we wonder, "Why is the sky blue?".
But deep questions about what is normal in the world they can change the outcome of.
And "Why is the sky blue?" is a deeper question than we think. Because it isn't blue everywhere in the world right now. And whether it is the fault of the US automotive industry, or aggressive investment by China in their own manufacturing base matters.
Of course, both have been responsible for pollution at different times. Growing up in Michigan near Dow Chemical I know this.
But depending on how they select training data answers can be nudged one way or another.
The US is smart too, and they have people working on the same things. It's ironically easier for me to talk about how China's security services are likely to shape their models' perception of the world. Which is a damn shame, because we deserve unbiased answers so we can all help our country improve.
Or countries improve if you want to take a global shared world view.
Sounds like they spent $40 million finetuning an open weight model on their own data? I wonder what they built on.
[1] https://www.businessinsider.com/thomson-reuters-builds-ai-mo...
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
That’s it? It was generally competitive with leading frontier models? Neat, but why would someone pay for frontier models and also a generally competitive additional product?
Looking forward to the ERP fine-tune.
It eventually stopped making sense because of inference costs. Running something internal with 30% GPU utilization is just too cost inefficient compared to using an API. Idk how Reuters will manage to solve this fundamental problem.
It won't match Anthropic or OpenAI, but it could be economic?
That's actually easy, because you can solve it through doing nothing and simply declaring that optimizing for lowest cost is not the main goal.
The fundamental-ness of that problem is entirely man-made and thus can easily be declared void as long as you have the cash to back that up.
Which might be a winning strategy in a world where everyone else is not doing that. Plus that your knowledge stays in-house, etc.
At the Thomson Reuters family of companies (technically then: Refinitiv Ltd. sold to LSEG), the first foundational model (in the sense of "trained entirely from scratch") was trained already in 2018 (i.e., pre-ChatGPT); it would even have been earlier, but the electricity wires and fuses in the rented 5 Canada Sq, Canary Wharf office had to be replaced first at the time to deal with the current needed to serve the GPUs.
https://huggingface.co/thomsonreuters/Thomson-1.0-Small
(Full disclosure I’m a TR employee, although I had nothing to do with making this)
Benchmark overall score comparisons: Thomson 1.0-Large - 78.5, Opus 4.8 - 79.5, Gemini 3.1 Pro - 78.0, GPT 5.4 - 76.5,
Interestingly, Journalism is not one of the benchmarks where Thomson fared all that well.
I mean that's a reasonable thing to do, but then the press release shouldn't be written the way it is written.
They're not as detached from the rest as the industry as the writing suggests
Which is the thing with press releases. Would've been nice to not do the bare legal minimum tho
But this is qwen based.
But w/e I'm pro AI so more companies having more people with skills for more post training is cool
Weird flex to boast that fine-tuning is cheaper than a full pre/mid/post-training run.
Someone else had to spend the billions to make and release the open model. That doesn't make your "different path" a breakthrough in efficiency and cost savings. Also you're not a frontier lab.
How I understand it, without really reading into it:
- Thomson Reuters did not "create" a frontier model, they gave some money, maybe a bit of their data to Imperial College London, and took Alibaba's Qwen 3.6 35B A3B Model for a basic fine tune.
- "They" (some undergrads at Imperial College London) used an existing ablation framework to undo some of the topic alignment of the original model.
- "They" fine tuned on some domain knowledge trying to preserve general knowledge - here maybe, just maybe some data came from Thomson Reuters.
As a result: one of 100's of Qwen 3.6 35B A3B sparse model fine tunes, just for publicity, to write overstating headlines like "X created their own frontier model"