The Ledger
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Frosst: Large language models are not a clear path to AGI
“I don't think
It's a clear path to AGI.
I don't think we're going to be making digital gods anytime soon.”
Frosst: Cohere is not chasing AGI, consumer bots, or public benchmarks
“So we're not interested in making like a public facing bot for consumers to talk to. We're not interested in chasing AGI. We're not interested in a lot of, there's a lot of like weird public benchmarks that exist trying to measure, you know, how close we are t…”
Frosst: AI enterprise value is in deployment and workflow, not weights
“When I think about Koshier's value, like it's not in, you know, the weights of any particular model. In fact, we we've released our model weights for researchers as well. So if researchers want to use our model, like they can download the weights from hugging …”
Frosst: LLMs Will Not Be Useful for Business Strategy
“I don't think we're going to get to a point where you will treat or think about an LLM as a person or a coworker. You will always know that it has very clear limitations and it can be useful when you give it the specific instructions of what to do, but it won'…”
Cohere's Nick Frosst: LLMs add the most value inside enterprises
“And where I think it is most useful is inside enterprises. So both for their internal use and for building stuff for pro for customers. But I think that's where it adds the most value.”
Frosst: Nobody was selling access to large language models in 2019-2020
“The market didn't really exist at that time. There was no, there was nobody selling access to large language models.”
Frosst: Companies will stop branding as 'AI companies' as LLMs become standard
“I think at some point, we'll get to a situation where companies are not AI companies the way companies are not, like, app companies anymore, right? Like, there was a time when everybody's like, oh, we got to make an app. We're an app company. We're going to ma…”
Frosst: Emotional stakes of startup stress feel identical at 6 or hundreds of employees
“When we were a, Six person company. The stakes felt just as high as when we are now a several hundred person company, customers all over the world, offices everywhere, you know, like the emotional impact feels the same.”
Cohere initially trained LLMs across fragmented GPUs due to resource constraints
“So when we first started training, we would I think we started training not with, like, a single kind of supercomputer, but by, with small amounts of GPUs spread out throughout a data center. And that is very, very slow, because you have to communicate between…”