First Time Founders: Is Cohere the Next AI Powerhouse?

17:12Scott Galloway:And when the history of AI is written a hundred years from now, a thousand 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.

17:26Ed Elson: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 this kind of technology into the hands of millions and then eventually hundreds of millions of people. is that when you see all of these big tech companies that are spending hundreds of billions of dollars building out their ai capabilities building out data centers renting compute buying chips uh and then spending money on on on models like like the ones you've built to their own products do you think it was sort of a moment where they kind of woke up to what the capabilities and what the prospects of AI could be because they just saw it a lot?

18:24Ed Elson:Or was it something else? Was it that the technology changed a fundamental way? I mean, to what extent was this sort of the narrative that suddenly captured people's imaginations versus something in the technology actually changed, which made Mark Zuckerberg think, now we need to get on this?

18:43Nick Frosst:I think everything we're experiencing today is largely predictable from around 2020, 2019.

18:53Scott Galloway:Now, that's not a coincidence.

18:55Nick Frosst:That's when I left Google to start Cohere with Aiden and Ida. So that's the reason why I think it was largely predictable around that time is because that's when I predicted it. So I'm sure other people predicted it before. I got on board at that time. at the time, I think when I remember telling people I'm going to leave to go create this foundational model, I don't think we use the word foundational model. We just said large language model company. We're going to be a large language model company. We're going to make large language models. I remember everybody saying, yeah, that's probably a good idea.

19:23Nick Frosst:I don't think anybody was thinking like, oh, that makes no sense. The question was not low limit. The question was like, oh, you know, is Google just going to do it? Are the other big companies just going to do it? But I think at the time, it made sense. Now, it really still wasn't popular. And when we had conversations for the first few years of Cohere's history, the conversation was, this is a large language model, and here's why we think it can help you. the conversation now is okay cool like why you know why your large language model or like how can this actually help me get into production how can i have it access my my private data without giving that away like how can i how can i deploy it in a secure and safe way so that i can handle regulated industries um how can i connect it to my specific data in an enterprise like those are all the questions we answer now so it's changed a lot and what changed in particular and a thing I did not predict at the time was the success of chat fine tuning.

20:22Nick Frosst:So you train this big generic language model. When language models were first created, what they did was they just completed the ends of sentences because they were trained on the web. So you really can think of it as like a web. We're calling it a large language model. At the time, it wasn't a large language model. It was a web text model.

20:40Ed Elson:Yeah. It's like a Reddit language model.

20:42Nick Frosst:Yeah. Yeah. So if you wrote the first part of a website, it would write the second part of a website. Not even the HTML, just the text on the website. And you could do a lot of stuff with that, but it was confusing and weird. And then OpenAI and a few other companies at the same time fine-tuned that large language model on chat dialogue.

21:04Scott Galloway:And that suddenly, suddenly people understood it.

21:07Nick Frosst:And at the time, actually, I remember thinking I was surprised at how efficient that was. Because when you think about it, you're training a model on the entirety of the web. So a huge amount of language. And then you fine tune it on a relatively small amount of chat. And yet, actually, it learns how to chat pretty well. So that is, I think, responsible for the difference between 2020 and 2022. It was the data efficiency of chat fine tuning that allowed people to like for the model to meet them where they're at. They kind of expected, users kind of expected chat to work when you told them it was a large language model, and it didn't.

21:41Nick Frosst:It was this like weird text thing. So then making it work in the way they expected it to work seems to have really gotten like woken people up to the effect and the utility of these models.

21:52Ed Elson:Yeah, and I'm sure it was also the volume too, the idea that if you keep on chatting with this AI, you're contributing more and more data for it to train itself on. um i i want to get to the specifics of cohere in a moment but you know it's it's interesting you're describing that the model gets better when it's subjected to or when it's fed large amounts of data and also like diverse forms of data and originally we were kind of just limited to the web but the web isn't all of life there's more beyond the web uh that these models could be trained on. And so, too, you could say the same thing about these chats.

22:34Ed Elson:I'm wondering if there's other forms of data that you think will be prevalent for model training in the future. You know, things in the physical world. I mean, typing words onto a keyboard and seeing words on a screen isn't everything. But to AI right now, it seems to be close to everything. So is there a way, are there other forms of data that you think in the future AI will be fed and therefore that would sort of take us, I guess, on the path to AGI?

23:12Nick Frosst:Let me first talk a little bit about the way we train these models. 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. Like a thousand people, a thousand 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 talk to the model, and if the model gives a good response, they say, that's great.

23:52Nick Frosst: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 trade the model on that SFT data. After that, you can do reinforcement learning, which is a type of machine learning invented before transformers, where you're training a model without access to the right answer.

24:28Scott Galloway: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.

24:35Nick Frosst: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 um your question about models being restricted to the to the web and missing the stuff in the real world. Is that a blocker to AGI?

25:12Nick Frosst: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 have, 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 I don't, there's a whole bunch of blockers to AGI. And that's just, 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.

25:59Nick Frosst: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.

26:18Scott Galloway:My time should be free to think strategically, to think creatively. There's so much work that a large language model, when connected into the things that I am using, can do for me and subsequently allow me to do the interesting in the human. And like, that's what I want to make. I want to make a technology that does that as good as possible.

26:36Ed Elson:Do you think that AI, the people building AI, the leaders of the AI industry, Sam Altman probably being the high priest right now, at least, do you think that there's not enough appreciation of that? Do you think that people are too obsessed with, we need AGI, we need human-like intelligence. I just look at the contract between Microsoft and OpenAI, which basically one of the stipulations in the contract is, you know, the terms will change once we achieve AGI. I mean, there are many questions like, what does that even mean? But the fact that AGI is sort of the benchmark for everyone, and I'm even asking you, like, How do we get to it?

27:20Ed Elson:Do you think there's too much obsession with this concept of AGI in the AI industry right now? Yeah. Yeah.

27:29Nick Frosst:Look, high priest is a good term. A lot of the thought around AGI and discussion around AGI feels religious to me. It's calmed down a little bit, right? Like back in 2023, like 2024, my views on this were a little heretical. people would disagree. If I said, hey, AGI is probably not around the corner, people would disagree and say, why do you think that? I would get a lot of pushback. I don't get much pushback these days. I'm like, yeah, guys, guys, guys, we know. Transformers, incredible, super awesome, super good, can definitely be way better than they are, need to be deployed correctly, need to have lots of stuff in order to get them into production.

28:08Scott Galloway:That's what we focus on. But AGI? No.

28:12Nick Frosst:And everybody's like, yeah, yeah, yeah, totally, I get it. And if you use a large language model, which as everybody does these days, You'll feel that pretty soon. You'll be like, yeah, they're amazing at these things. And then I ask them some other things and they don't understand at all. And they have a completely different, it's very different talking to a language model as it is chatting to a person. And people kind of know that when they're grounded in an environment. The focus on it is, I think, a narrative device more so than it is a scientific belief. We'll be right back.

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29:55Ed Elson:We're back with First Time Founders. I'm going to ask you the question that you said everyone asks about Cohere, which is, what is the difference between Cohere versus the other foundation model companies? What is the difference between Cohere and OpenAI? Between Cohere and Anthropik? Those are the two big ones in my head. What is the difference?

30:16Nick Frosst:The big difference is we are not a consumer company and we're only an enterprise company.

30:22Scott Galloway:So we can't pay$20 a month to get access to our tech. We're not trying to build a product that people use in their personal lives. We are instead selling only to large and medium enterprise companies.

30:38Nick Frosst:and we create language models and search models and an agentic framework for using them that is tailored to the needs of those companies. That strategic difference comes from a philosophical difference, which is a different view on the technology, right? Like I don't think the technology is going to get us to AGI. And I don't think the biggest utility of the models is in people's personal lives. I think the biggest utility of these models is in work. I think

31:05Scott Galloway:Their ability to augment and automate work at a desk behind a computer is, I think, what they are the best at.

31:13Nick Frosst:And so we have that different view of the technology that leads us to think differently about where we can add the most value to the world. And that leads us to being an enterprise, a singularly enterprise-focused company.

31:24Scott Galloway:And that is, as mentioned, unique amongst the foundational model players.

31:29Ed Elson:Just so we can picture like what kinds of work is being done, what is an example of a use case that an enterprise is adopting because of using Cohere as a foundation model?

31:41Nick Frosst:Lots of people will go into work, open up North, which is the name of our agentic platform. So it's like a chat app with automations and you can make custom agents and you can share those across people. But it's a chat app that on its surface you would be familiar with. So they'll go into work, they'll open that up, and they might open up our model and say, you know, hey, you know, somebody emailed me yesterday about a brief for a meeting, read that email, then cross-reference that with our Salesforce data, and then make a table sharing, like telling me the state of that customer. That's something the model can do for it.

32:16Nick Frosst:Or they might say, hey, you know, I just got this data room from this company, I'm trying to evaluate, read through the data room, do some analysis, come up with a cited

32:25Scott Galloway:and detailed document on how you think that company looks, and then send a Slack message to my coworkers with that PDF.

32:33Ed Elson:Just looking at where you are in the AI world, you are automating tasks that are done at businesses and enterprises that, as we are all talking about, would otherwise be done by humans, which introduces the question of, is AI going to replace people? And this has been a large debate. We're obviously seeing a lot of layoffs in tech right now. A very charged debate. How do you think about all of this? How do I think about it? Frequently.

33:10Yeah.

33:11Nick Frosst:So there's a lot. So I think this technology is, you know, there is a huge amount of stuff that people do that large language models should be doing for them.

33:18Scott Galloway:large language models will do a better job of them. The work itself is not very enjoyable. Humans are really good at a lot of stuff that large language models are very bad at. And largely, they enjoy the stuff that they're good at and don't enjoy the stuff that large

33:29Nick Frosst:language models are good at. So I think, you know, in the same way that we've had previous industrial revolutions that augmented and automated a huge amount of stuff that people

33:41Scott Galloway:generally didn't really like doing.

33:43Nick Frosst:And we look back on those periods of time as kind of chaotic, but largely a good idea. No one's running around saying, hey, the steam engine was a step in the wrong direction, or hey, the industrial revolution was bad. We should all still be farmers. I think there's something similar going on with this. Now, I do think this technology is fundamentally augmentative, right? I think this technology, anybody working behind a computer, I think this technology can automate, I don't know, 20 or 30 % of their work.

34:17Scott Galloway:I don't think it can automate 100 % of pretty much anybody's. Huge amounts of the work that we do is not just text on a computer or images on a computer. It's personal.

34:27Nick Frosst:It's understanding the cultural context. It's talking to people and coordinating and aligning. It's thinking strategically. It's doing all of this stuff. And that's true at every level of an organization.

34:38Scott Galloway:So I think there's a lot of people say, oh, this is going to take out the bottom bit of an organization. I'm like, no. What this is going to do is make, augment and improve and increase efficiency and productivity across the entire organization. Is that going to have consequences on the labor market? Yes, absolutely. It is. Just as the Industrial Revolution had huge consequences on the labor market. Just as, you know, the widespread adoption of computers had huge influences on the labor market.

35:05Nick Frosst:Like in our lifetime, you're in my lifetime, we have seen wild changes in the way that work is done as a result of technology. It was not so long ago that every organization had a huge number of people working as typists to type stuff up because

35:20Scott Galloway:people didn't have computers and that needed to get done. That doesn't exist anymore. But the labor market evolved. The labor market figured out all those people still are doing good work, just doing different work. So I think that this will have similar effects to the computer, to the internet, to the industrial revolution on the labor market. And I think governments and organizations and unions and businesses should be thinking about how to make sure that that goes well, how to make sure that that is largely, that that uplifts people and that builds a resilient economy and that allows people to do things they like to do.

35:54Scott Galloway:And I really like, that's the conversation that I'm encouraging everybody to have. Like, What are the policy decisions that can be made in order to make sure that that is good for all people? But I think recently, like all the talk of, you know, there's been a lot of tech layoffs. And I know that that's kind of tried to be tied towards AI. I think that's a lot more related to the overhiring that happened during the pandemic than I think it's related to having those people suddenly like an AI is doing that job for them. So I think that's kind of borne out if you look at the data. So I do think it's going to have consequences on the labor market.

36:31Scott Galloway:I think when history looks back at this, we'll largely say that it was a good idea. The same way people say the computer was a good idea, the same way people say the Industrial Revolution was a good idea. But it is going to be a chaotic moment, and it does require 10. Do you have concerns about what this will do in terms of inequality?

36:49Ed Elson:I mean, I think about the downsides. I think long term, it's value accretive, which means that's a good thing for society in the same way that the steam engine was, the internet was. But I think the big concern that seems super likely to me is that the value is accrued to the people who own the AI and that, yes, maybe some of us might be getting some value out of using AI, but we won't be the ones who own it. And it will only make wealth inequality even worse, which could have all of its own impacts. Do you worry about that? I do worry about that.

37:30Nick Frosst:Yeah. So income wealth inequality is the thing I, is one of the things I think is the most pressing issue.

37:36Scott Galloway:I think, yeah, I think it's one of the most pressing issues for the world right now. And I do worry that this technology, similar to other technologies, stands to exacerbate the wealth inequality that was already rising over the past few decades. I think the correct solution to that is policy. Oftentimes when people thinking about the economy, they kind of forget that this is a system we create. And it's a system that can be subtly pushed in one direction or another direction. And you can add policies, you can change things in order to make sure that this works for everybody, for all people in your country or in whatever organization you're within.

38:17Scott Galloway:I think that's the conversation I want the world to be having. And one of the reasons why I'm very vocal about saying, hey, I don't think we're getting to AGI is that the AGI conversation often distracts from that conversation. Because if you're talking about, oh no, what if we make a digital god and it kills all people? It's very difficult to have the conversation, hey, do we have the right policies in place to encourage better income distribution such that We don't end up in a bifurcated society, which I don't think anybody wants.

38:47Ed Elson:What kinds of policies or what do you think that would look like is my first question. And then my second question is, as someone who cares about that, are you a pariah in the tech industry? Because from my understanding, there is a feeling of if you're talking about policy and regulation, then you're a Luddite and you're just trying to hold AI back. and you're just scared. So I guess, how do you think about those two questions? Am I a pariah for talking about that stuff?

39:25Nick Frosst:No, no. But I also don't live in Silicon Valley, right? Like I don't, I live in Toronto. I'm certainly in the tech scene. You know, I talk to VCs all the time. I talk to other tech people.

39:40Scott Galloway:I talk to, you know, lots of people thinking about this. but would I, you know, would I be a pariah in, I'd certainly have, I certainly have lots of different views than people would have hanging out in the remnants of the effective altruist parties in Silicon Valley, right? I certainly have very different views than, than the culture that developed there. I mean, am I, like, I'm certainly not a Luddite, right? Like, I'm certainly not

40:08Nick Frosst:against the creation of technology. But, you know, having been to, where was that town I went to?

40:16Scott Galloway:There's a town in England that was kind of the epicenter of that. And I went to a museum there on Luddites that was very interesting.

40:22Ed Elson:I wish I had it, but I know I've studied it. I know what you're talking about. I wish I had them there.

40:28Nick Frosst:Yeah, you know, and a lot of the people at the time were, you know, they were frustrated at the loom for making their economic situation worse.

40:37Scott Galloway:Right. now i again we all look back at the automated loom and we think that was a good idea and the economic situation that people live in now is better than the economic situation that they were living in during that time um but i'm empathetic i'm empathetic to saying hey like my economic situation is shitty and it's shitty at a systematic level at a population system level let's figure out how to how to make that better right so i am empathetic with that um so would I be a pariah? I don't think so. I think actually a lot of people know this. I think if you go to Silicon Valley and you tell somebody, hey, income inequality is bad, it's hard to live in that city and not think that.

41:15Ed Elson:Just looking at the future of Cohere, reportedly Cohere is looking to go public. I don't know if you can talk about that, but that's what we've been reading. Can you tell us about those plans?

41:29Scott Galloway:Aidan and Ivan and my goal in creating this company was to create something that outlasted us was to create a generational company like that's motivating that's exciting that's a fun thing to be part of that's what we're excited about um i think the right way to do that is to become a public company i think that's how you can build i think that's like i i like those mechanisms i think that's how you can build a company that is bigger and longer than you and that's exciting i think the tech we're building is speaks for itself and is getting there. I think the customers that we've closed and the relationships we've built and the value we've been able to add to our customers is something I'm enormously proud of and something I want to keep doing and something I think would be best done through a public company.

42:17Ed Elson:We'll be right back.

42:31Ed Elson:We're back with First Time Founders. We've discussed the decline of the IPO on our markets podcast a little bit, just the fact that there are fewer public companies in America than ever. And also the fact that so many of these massively transformative companies are taking so long to go public. The idea that OpenAI is only now, I mean, they have the nonprofit issues, But the reality is this company is supposedly going to go public at a trillion dollar valuation. That's crazy. So how did you balance? It seems as though the startup world is more interested in staying private as long as possible for, or at least that's what the data would tell us, versus going public.

43:18Ed Elson:So how did you think about going public? What are the pros? What are the cons? And why now-ish?

43:26Nick Frosst:Well, I've made no promises to timing. Yeah, fair enough. No promises to timing. But I do think, look, I don't, yeah, again, I don't know what OpenAI is doing.

43:36Scott Galloway:I think there are very, I look forward to the interesting books that will be written about that company over the next 10 years. I don't know what's going on. And I don't know when they're going to go public. I know they've announced stuff. I don't know. It's a very, it's its own beast. It's its own unique and interesting company. I'm sure lots will be written to describe that story. For us, our business looks a lot different because we don't have a consumer offering. We don't have the same losses that they have. We're not losing money on every customer. Our margins are actually, they look a lot more like SaaS margins.

44:13Scott Galloway:When we work with a customer, we deploy our models into their environment, right? And that allows that model to access their private data securely so that we can't see it. it also makes the nature of our margins completely different um you know i think that puts us in a very different position than the consumer companies that are out there and i think that puts us in a position that makes it a lot more resonant with a public market it's a lot more understandable a lot a lot more uh yeah it looks a lot better um so i i do think the right way for us to make sure that this company outlasts us and continues to deliver is to eventually go public.

44:50Scott Galloway:Um, when, yeah, I don't know. And there's an interesting thing you're pointing out about there being a smaller number of public companies. There's an interesting thing you're pointing out, uh, about companies staying private for longer. I don't think those are unrelated to the income inequality, wealth inequality stuff that we were talking about earlier. There's an interesting dynamic in the economy going on right now. And like all of those things are kind of related. um but for us like yeah no that's that's what we're going for um and i think that's the path

45:20Ed Elson:that we're on you are a canadian company you're based in canada uh how do you think about ai as a sort of international geopolitical race um we've got some big ai companies in america some big AI companies in China. I guess Mistral is another one that's in France. And there's us in Canada, and that's it. Right. Yeah, there are four countries in the world

45:48Nick Frosst:that can make this technology.

45:50Ed Elson:Tell us more about what that means for society.

45:55Scott Galloway:Yeah, that's a strange one. I think that's when you think about how difficult it is to make this technology and how resource intensive it is. It's not super surprising that there aren't, like just in it's not surprising that there aren't that many companies doing it it's not surprising that there aren't that many countries so but it is it is a strange reality that yeah there are four countries in the world that can build this tech when i think about the what that means for geopolitics i think this technology is best in the same way you know i use this analogy of like rockets it's like building rockets um another analogy is to say it's like building power plants.

46:34Scott Galloway:It's like building infrastructure. The technology is a lot more like infrastructure than previous computer science efforts. And so I think it's a good idea for countries to have the ability to build infrastructure themselves. It's a good idea for countries to be able to build their own nuclear power plants. That's useful. That sets up the country for success. It's a good idea for them to be able to build their own roads. like infrastructure for people is good and it's good for countries to be able to do that from a strategic perspective from a security perspective like from an economic perspective it's generally a good idea so i think this technology is important for countries to be able to build i think there's ways that that countries can work with the providers in order to give that ability to their country um and i think that's something that a lot of the world is seeing right now you know we had two decades of the history of tech really being american really being centered on america and america is a dynamic fast-moving ingenious place that will is going to continue to be defining on the technology and technology in general but i do think it's good for the world to have tech that comes from other places you know to have a more distributed um view on on how technology is developed and what it can do for people so that's one of the reasons why i'm happy to be building out of canada is that the the thing that

47:59Ed Elson:changes the trajectory of geopolitics. For example, you made the comparisons to other technologies. I think some people would also make the comparison to the nuclear arms race, not to say that AI is like a nuke, but to say that it was the belief of nations that this is what will tilt the balance of power across the world. We have to build these things because if we don't, and if Russia or anyone else gets their hands on this transformative technology, it will completely upend the geopolitical structure of Earth. Do you view AI in the same way? Not in the sense that it would be, I'm not making a nukes and it's going to be destructive point.

48:42Ed Elson:I'm making a point of the power of it. Is it a question of whoever builds the AI first and whoever builds the best AI, they will be the most powerful force in the world. Do you see it that way?

48:57Scott Galloway:No, that's a little extreme. Yeah, I see it as like a strategic and imperative for countries to have this technology to facilitate economic growth, to do stuff in the same way I see it's imperative for them to build roads, really great healthcare, build nuclear power plants, build wind, build other pieces of infrastructure. I don't think I would go so far as to say it will be the defining thing. Certainly, yeah, the nuclear bomb analogy I disagree with. And I know that's often used when people are talking about AI as an existential threat. But again, because I don't think transformers are going to get us to AGI, I don't think they pose an existential threat.

49:36Scott Galloway:And so I don't think that analogy serves us in conversation. I do think it's important to think about the technology as infrastructure and infrastructure that's good to build. But one piece of infrastructure amongst many pieces of infrastructure that are good to build and important to build in this moment. And we're certainly in a dynamic and changing geopolitical time, right? Like this, you know, these are unprecedented times, as has been said for the past decade. But yeah, and I think the technology will have an impact on that, but I don't think it will be the defining thing.

50:09Ed Elson:You are one of the leaders in AI, which is the most important and transformative of technology, it's certainly in my time, I'm Gen Z, I was not there to see the internet be created and built. So I think, you know, this is an extremely important moment, not just for America or Canada, but for the world. Does that weigh on you? What is it like to be a founder who is at the forefront of this world-changing technology?

50:45Scott Galloway:yeah it weighs on me yeah it's totally a strange place to end up in and not a place i thought i would it excites me i love working with cohere uh working at cohere i love working with all the people at cohere and i get really excited and i'm enormously proud and occasionally deeply moved by the work that we're doing and the group of people that i get to spend time with working on it it is a complicated emotional experience to think hey this technology is you know the defining narrative and we are one of 10 companies in the world four countries in the world that are building it you know I still love I mentioned earlier like I still I love the tech and I'm moved by it and that's that influences how I think about this like I think we're building something beautiful and cool and can be useful and it's it's very meaningful to the world and to the you know to the people around me and that's interesting there is a subsequently a pressure and an intensity that I did not anticipate when we started this company.

51:45Scott Galloway:I don't think anybody did. I think I solved that by staying grounded in things that have nothing to do with tech sometimes. I think that's an important part of the way that I live my life, is by doing stuff occasionally that is completely unrelated to AI, to transformers, to tech itself. And I think that That might be why I have pretty different views than the rest of the people in similar positions to me.

52:15Ed Elson:A lot of young people watch this show. What would be your advice to young people? Not necessarily just founders, but I think young people in general, perhaps even young people who are concerned about their job prospects, their career prospects, people who believe that AI could be taking their jobs. I mean, from the guy who's building the AI, what would your advice be to young people? terms of jobs my advice is to it has been the same for young people for a while which is that i know

52:46Nick Frosst:i meet a lot of people young people who are like anxious about making the right decision like i got to work on this because that's going to be the right thing or that's going to be the right thing and my my advice has often been look the world's too chaotic for you to predict what's right like you can't like every every year you could read an article of somebody saying the next big job is this and you got to go into this and they're almost always wrong and so it's just too chaotic, you can't predict it.

53:10Scott Galloway:What you should instead do is focused on what you're interested in. And what you can optimize for is your own excitement, your own curiosity, your own interest. And when you're thinking about what career you want to pick or something, you should first and foremost be like, well, what am I excited about? What am I interested in? And conditioned on that, your ability to be successful is much higher than conditioned on you choosing what you think is the optimal decision at that moment. So I would really encourage young people to follow their curiosity, follow their passion more than they think follow what's optimal just because you can't predict it

53:43Nick Frosst:it's really hard um my other advice is to when the central when the narrative around the world these days is one of like it's an unprecedented chaotic absolutely crazy time i definitely encourage people to learn about history just read just whatever history from whatever time like whatever you find ancient history prehistoric prehistory um you know enlightenment history

54:05Scott Galloway:modern history, like wherever, literally wherever. Yes, we live in unprecedented times. Yes, stuff is chaotic and weird right now. And I think when the history of this moment, I think people are going to read the history of these few decades with curiosity in the future. But there's been a whole lot of crazy times. There's been a whole lot of absolutely nut stuff that has happened in the history of humanity. And it is calming sometimes to read about those and understand the good things that happened, the bad things that happened, the way stuff continued in the face of it, I find that grounding.

54:42Scott Galloway:And that grounding is helpful for keeping you focused on like what you're interested in, what you're passionate about, what you're curious about.

54:47Ed Elson:Nick Frost is the co-founder of Cohere. Nick, this was great. We really appreciate your time. You as well. Thanks for the conversation.

55:00Ed Elson:This episode was produced by Alison Weiss and engineered by Benjamin Spencer. Our research associates are Dan Shallan and Kristen O'Donoghue. And our senior producer is Claire Miller. Thank you for listening to First Time Founders from Prof. G Media. We'll see you next month with another founder story.