The Prof G Pod with Scott Galloway
First Time Founders: Is Cohere the Next AI Powerhouse?
01 Mar 2026
Chapter 1: What is the main topic discussed in this episode?
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Welcome to First Time Founders. I'm Ed Elson. Artificial intelligence has become one of the most heavily funded sectors in the world. More than 30 startups have raised over $100 million this year alone.
Chapter 2: What is Cohere and what does it do?
As AI becomes more embedded in how the world operates, a handful of firms have emerged as the key players behind that transformation. Among them... is a company building the kind of AI most people don't see. That is the AI that is powering the systems that run businesses and governments.
Founded in 2019 by three former Google engineers, this company has focused squarely on the enterprise market, developing large language models for clients like Dell, SAP, and Salesforce. It even recently signed a deal with Canada's government to bring its technology into public operations. Now valued at nearly $7 billion,
It has earned a place alongside giants like OpenAI and Anthropic, helping define what the next era of AI will actually look like. This is my conversation with Nick Frost, co-founder of Cohere. All right, Nick Frost, good to have you on the program. Thanks for having me. So for those who don't know what Cohere is, I think we should probably just start there. What is Cohere? What does Cohere do?
What are you guys building in AI? So we're a foundational model company.
and we are uniquely and singularly focused on the enterprise. There's about 10 companies in the world that can make foundational models. So foundational models, the large language models that are largely these days synonymous with AI. If somebody's talking about AI, they're probably talking about large language models. There's about 10 companies in the world that can make them.
We are unique amongst them in our singular focus on the enterprise. So we make large language models that are good at the stuff that enterprises need them to be good at. We make them easy to deploy and efficient to deploy for enterprises. We deploy them securely and privately so that we can't see the data that our customers are passing into the model.
That allows them to access the truly useful data out there. And we make them easy to work with via an agentic platform. So we do kind of the whole thing in order to get AI to work at work.
So these foundational models, I think most people who are interested in tech kind of know what they are, but just at a very basic level, the foundational models are the models that all of these AI startups are building off of.
or all of these companies, if you're building AI or you're building AI products, you need the foundational model, which companies like OpenAI and Anthropic and Cohere, your company, are building. What am I missing?
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Chapter 3: Why did Cohere choose an enterprise-only strategy?
for decades until around 2011, 2012, they were finally able to show that neural nets were suddenly the best at image recognition. That was the first thing that they really knocked out of the park on. And that was done at U of T with a bunch of other brilliant U of T students. The reason we are where we are with neural nets in general, which of course is the precursor to transformers, right?
So there's kind of, if you think of it broadly, there's like AI as a concept, there's machine learning as one strategy for doing that. Neural nets as one strategy for machine learning, transformers as one type of neural net. That's kind of where we are. So the neural net part in particular, Jeff can claim a huge amount of responsibility for.
And it's really his tenacity and his dedication to continuing to work on it, even when everybody else around him was saying, no, this is a bad idea. It's not going to work. But we have to thank for where we are today.
So when we look through the history books written about AI, I mean, AI is having its moment right now. What changed? I mean, AI had been worked on and neural nets had been, people had been working on this stuff for decades. Jeff Hinton had been working on it for decades. He makes this breakthrough with image recognition in the early 2010s. Now it's ubiquitous. Was ChatGPT the breakthrough moment?
Like, what will the textbooks tell us about what changed when AI became mainstream? There have been other AI moments.
There have been other times when people are, when the whole world's really thinking about AI. This is the first time that I would say it's been this dominant narrative of the economy for the past few years. And that's a first, like in technology, it's been the dominant narrative of technology for the past few years.
And it's been the dominant narrative of the economy even more for the past few years. So that's kind of a first. But there have been moments where people have been as really excited about AI and thinking that they're in some kind of AI moment before. You got to separate AI as a property versus any implementation trying to get at that property.
So people have been thinking about artificial intelligence, like what happens if a machine has intelligence the way a person has intelligence for a really long time. There's a myth that I cite pretty often that was written in like around, you know, 1500s, 1400s, I believe. Yiddish myth about the golem, which talks about, you know, some rabbi imbuing intelligence into a clay man.
And then he asks the golem to go get fish from the river. And then he leaves his house for a little bit. And when he comes back, the house is filled with fish and the river is empty. And like, it's a joke, right? Like it's effectively a comedic story that's told at that moment. And the intelligence is complicated and there's nuance in language.
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Chapter 4: How does Cohere differentiate itself from other AI companies?
They do all kinds of things that there's no other way we could get them to do yet. And transformers in particular, large language models, are very easy to use for the average person. And that is, I think, really why this feels different. So if you look at the other moments when people were talking about AI, like Deep Blue, let's look at that one as an example.
You can read tons of articles about people talking about what's happening with the machines. Are computers getting as smart as people? They beat the best chess player in the world. What's going on? But if you're an average person, you couldn't really interact with that. Maybe if you're good at chess, you could try the chess bots. And people did.
And actually, you know, chess, in some ways, chess is more popular than it has ever been before. And in part, that's because you can be at your home playing against something better than a grandmaster. But you could interact with it that way. You couldn't really interact with a search algorithm like an A-star search algorithm in anything else. So your experience of it is pretty limited.
Same with machine learning. Like when we made image recognition, the best image recognition model, suddenly, yeah, your phone, you could go on Google Photos and you could search up pictures of, you know, dogs and see all the pictures of dogs you've seen over the years. Like that's new, that's cool. But you couldn't, that's still directive.
That's still like somebody made the model that does the thing. It's telling you how to use it. Transformers are the first time that any person without any experience in computer science or AI can go up to the model, open up a chat window, ask it to do something, and it'll do it. Or it will not do it, and that'll be interesting itself.
But you can interact with it without it being prescriptive of how you interact. And that's, I think, the reason why this is suddenly happening. so much bigger. It's suddenly so much more interesting, so much more widespread and why it's become the dominant narrative of tech over the past few years.
So when people write the history of AI, and I want to be clear that I think the history of AI is not done. I don't think transformers are going to get us to artificial general intelligence. So I think there's going to be more waves of new, independent, spontaneous inventions. I'm sure that's going to happen. But I'm convinced that the transformer is going to be a central component of that.
And when the history of AI is written 100 years from now, 1,000 years from now, this moment will be talked about as relevant and interesting and a moment when a lot of stuff happened really quickly as a result of the tenacity of a handful of people.
Yeah, it's interesting that in a way it was the consumerization that really took things in a completely different direction, which is almost a testament, not necessarily to the underlying technology, but almost to like the productization and being able to put these kinds of technology into the hands of millions and then eventually hundreds of millions of people.
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Chapter 5: What challenges do foundational model companies face?
Okay. So the first step is to train them on everything on the web, everything on the open web. So you create a data set of all the text that's available for training from the web, and that turns out to be a huge amount of text. Orders of magnitude more text than you will ever read. I like... Like 1,000 people, 1,000 years reading 24 hours a day volumes of text. That's how much text.
So first step is train on that. Then you make a data set with people. So you have people create, like talk to the model. And if the model gives a good response, they say that's great. If it gives a bad response, they say that's bad. And they write what the model should have said. If you do that process, you'll create both ratings, like is it a good response or a bad response?
And you'll also create what's called supervised fine tuning data, SFT data. So that's like, here's the input to the model and here's a gold standard of what a person wanted. Like they wrote out the sentence, like that's what the model should have said. That's called SFT data. So then you train the model on that SFT data.
After that, you can do reinforcement learning, which was a type of machine learning invented before transformers, where you're training a model without access to the right answer. The model kind of tries stuff and then you say, hey, this was better or this was worse. And you update the weights of the model based on that signal. So then you can do reinforcement learning.
Now we do a whole bunch of reinforcement learning with synthetic data. So now we use the model itself to generate data and then do reinforcement learning on that synthetic data. So that's a big component of training now. So there's like the data you get from the web, the data you make with people, and then the data you make with the model itself.
And those, all of those are super relevant for making the models that people use today. Your question about models being restricted to the web and missing the stuff in the real world, is that a blocker to AGI? Like, yeah, definitely, that's a blocker to AGI. If when you say AGI, you mean human-like intelligence... Yes, that's a blocker to AGI. We are embodied creatures.
We learn our intelligence through interactions with the real world and intervention into the real world. There's lots of interesting psychological work that suggests learning and interaction are super related. So interaction is super important. Is that a blocker to AGI? Like, yeah, definitely. but there's a whole bunch of blockers to AGI, and that's just one of them.
And the technology as it exists today is massively impactful, massively useful, absolutely transformative on the nature of computers and subsequently the nature of work, massively transformative on the economy in general, then I don't think it's AGI. And nor do I think the transformer alone will get us to AGI. Nor do I care.
I don't really look out in the world and say, oh, geez, I wish my computer was a person. I look out in the world and I say, oh, man, there's so much stuff that a computer should be doing and not me. My time should be free to think strategically, to think creatively.
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