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40M$ to get a marginally better model is surprising, why not just use the free weight models

by make3 23d ago

adventured 23d ago
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.
- make3 23d ago
  they should unite to block all news from getting to LLMs without LLM providers paying per access
- bryanrasmussen 23d ago
  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.
jll29 23d ago
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.
- simianwords 23d ago
  I’ll bet against this because of bitter lesson. Generalised models with context engineering will be more reliable and cheap than training your own.
dgellow 23d ago
[dead]
jgerrish 22d ago
Because what these models are trained on matters. 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.

40M$ to get a marginally better model is surprising, why not just use the free weight models

by make3 23d ago

40M$ to get a marginally better model is surprising, why not just use the free weight models

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.

make3 23d ago
they should unite to block all news from getting to LLMs without LLM providers paying per access
bryanrasmussen 23d ago
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.

jll29 23d ago

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.

simianwords 23d ago
I’ll bet against this because of bitter lesson. Generalised models with context engineering will be more reliable and cheap than training your own.

dgellow 23d ago

[dead]

jgerrish 22d ago

Because what these models are trained on matters. 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.