Topic
31 facts from 3 episodes, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
2019
Aidan Gomez founded Cohere in 2019 and, as a Google Brain intern, co-authored the 2017 paper 'Attention Is All You Need'.
“Aidan founded Cohere in 2019, but before that, during his time as an intern at Google Brain, he was a co-author on the landmark 2017 paper.”
2019
After GPT-2 was released, Gomez called his future co-founders Nick and Ivan and proposed building large language models.
“I called up Nick, I called up Ivan, my co-founders, and I said, You know, maybe we should figure out how to build these things.”
2023
Gomez says many enterprise proof-of-concept AI projects in 2023 failed because of unfamiliarity and common implementation errors.
“And so in 2023, there were a lot of these POCs, a lot of people trying to get familiar with the technology, wrap their heads around it, and a lot of those POCs failed.”
2024
Cohere was described by the host as valued at more than $5 billion in 2024 and as providing AI language models and solutions for businesses.
“a company valued at more than 5 billion in 2024, which provides AI powered language models and solutions for businesses”
Undated
Cohere builds AI models and makes them available to enterprises rather than building a consumer chatbot.
“We build models and we make them available for enterprises.”
Undated
Aidan Gomez said Cohere had about 350 employees at the time of the interview.
“we're about 350 at the moment, which is insane to me”
Undated
Aidan Gomez said Cohere began with three founders working in a space he described as basically a closet.
“it was just Nick, Ivan and I. We were in like this tiny little. It was basically a closet”
Undated
Aidan Gomez said Cohere was founded to give enterprises AI from an independent company not tied to any one cloud provider.
“the world needed Cohere and the ability to adopt this technology from an independent, an organization that wasn't tied to any one cloud, any one hyperscaler.”
Undated
Aidan Gomez said Cohere's work requires spending hundreds of millions of dollars a year.
“In order to do what we do, you need to spend hundreds of millions of dollars a year.”
Undated
Aidan Gomez said Patrick Lewis, whom he credited with inventing retrieval augmented generation (RAG), leads Cohere's RAG effort.
“we have the guy who invented RAG, Patrick Lewis, leading that effort at Cohere”
Undated
Aidan Gomez said Cohere spends only a fraction of what some competitors spend on compute while serving the needs enterprises actually want.
“we might spend like twenty what some of our competitors spend on compute”
April
Aidan Gomez said Cohere's Command R and Command R Plus models were first made available on Microsoft Azure.
“when we released Command R and Command R Plus, our our new models, they were first available on Azure.”
Undated
Aidan Gomez said Cohere honors robots.txt and code licenses when collecting training data from the web.
“When we're scraping code, we look at the licenses that are associated with that code.”
Undated
Gomez says Cohere will not build a ChatGPT competitor and will instead build a platform and products that help enterprises adopt language models.
“We're not going to build a chat GPT competitor. What we wanna build is a a platform and a series of products to enable enterprises to adopt this technology and and make it valuable.”
Undated
Gomez describes a healthcare use case where a model reads a patient's full longitudinal record alongside the reason for the visit and produces a briefing for the doctor.
“but what we can do is we can feed that entire history in alongside the reason they're coming in”
Undated
Gomez says a Cohere-built research assistant let an insurance company's actuaries respond to RFPs much faster and win more of them.
“And it dramatically sped up their ability to respond to RFPs, and so it grew their business, uh, cause they were just winning many more of them.”
Undated
Gomez says Cohere can deploy on premises, inside a customer's virtual private cloud, or outside it, which helps with regulated and sensitive data.
“flexible to deploy on prem, if you want us in VPC, outside of VPC, literally whatever the customer wants”
Undated
Cohere partnered with Fujitsu, described by Gomez as the largest systems integrator in Japan, to build a Japanese language model that required pretraining from scratch.
“we partnered with Fujitsu, um, who's like the largest SI in in Japan, um, to build a Japanese language model.”
Undated
Gomez says Cohere recommends customers start with fine-tuning, then move to post-training and, if needed, partial pretraining.
“What we usually recommend to customers is start from the cheapest, easiest thing, which is fine tuning, and then work backwards, and so start with fine tuning.”
Undated
Gomez says Cohere must pay for a supercomputer that costs hundreds of millions of dollars a year.
“we have to pay for a supercomputer and those things cost hundreds of millions of dollars a year”
Undated
Gomez says Cohere's strategy is to stay about six months behind the frontier and sell enterprises models at sensible prices, rather than spend billions a year.
“To be at the front, be six months behind and offer something to market to enterprises that actually fits their needs at a price point that makes sense for them.”
Undated
Gomez says fine-tuning open models such as Llama is less effective than building a model yourself because you lack control over the training data.
“Taking those models and trying to, um, fine tune them, it's just it's not as effective as building it yourself, and you have much fewer levers to pull.”
Undated
Gomez says OpenAI was first to put reasoning models into production and that Cohere has been working on them for about a year.
“of course, OpenAI was the 1st to put it into production, but Cohere's been working on it, uh, for about a year now.”
Undated
Gomez says reasoning models give model builders a second way to improve performance: customers can pay for more inference-time compute instead of waiting for a new training run.
“There's now a 2nd lever, which is you can charge the customer more.”
Undated
Cohere has two core pillars: building its own foundation models and an agentic AI platform that Joelle Pineau calls the core of its product strategy.
“we do build our own models, and the other one is this agentic AI platform that we build, which is really the core of the the product strategy”
Undated
Cohere's North is an agentic AI platform designed to take the company's models and deploy them to businesses and governments.
“we have North, an agentic AI platform. It's designed to ingest the models that our modeling team is doing and deploy that”
Undated
Joelle Pineau says Cohere's platform can be deployed on-premise so agents can use a company's internal data without that data being sent to Cohere.
“you can run locally with your own data, so you can have a conversation or your agents can see actually all the internal data from the company without that data coming all the way to Cohere”
Undated
Joelle Pineau says enterprise customers still expect platforms to provide high levels of privacy, unlike consumers who have largely accepted responsibility for their own data.
“On the enterprise side, there's still an expectation that the platform will will provide high levels of privacy.”
Undated
Cohere evaluates models using sanity-check benchmarks, benchmarks that drive progress, and a third group of experience tests that use A/B testing and human evaluators.
“And then we have a third bucket that's more the experience, you know, we have a whole harness for AB testing and for bringing in human evaluators”
Undated
Joelle Pineau says Cohere has teams in Canada, the UK, France, Germany and the US.
“We have teams based in Canada, teams in the UK, in France, in we have folks in Germany, we have folks in the US.”
Undated
Joelle Pineau says Cohere's research-led focus on multilingual models opened commercial markets in Korea and Japan.
“it's opened up new commercial markets because when we get to Korea and to Japan, you know, of course, there's like, oh, you have world-class models in the local language”
Episodes