20VC: Cohere Founder on How Cohere Compete with OpenAI and Anthropic $BNs | Why Counties Should Fund Their Own Models & the Need for Model Sovereignty | How Sam Altman Has Done a Disservice to AI with Nick Frosst

Summary

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Podcast Summary

Episode Overview

Podcast: The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
Host: Harry Stebbings
Guest: Nick Frosst, Co-founder of Cohere
Episode Date: September 1, 2025
Main Theme:
In this episode, Harry Stebbings sits down with Nick Frosst, Canadian AI researcher and co-founder of Cohere, to unpack Cohere’s strategy in competing with OpenAI and Anthropic amidst the billion-dollar AI wars. The conversation dives deep into Cohere’s focus on enterprise, the real bottlenecks for AI progress, skepticism around AGI rhetoric, the need for model and technology sovereignty, and how hype distorts the real promise (and pitfalls) of modern AI. Nick gives candid and contrarian takes on benchmarks, talent wars, funding, societal impact, and the responsibilities of industry leaders.

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Key Discussion Points & Insights

1. Lessons from Geoff Hinton and Early AI Days ([05:03])

• Nick’s Experience with Hinton:
  Nick shares how working under Geoff Hinton at Google Brain shaped his playful, intuitive approach to research. Hinton encouraged creative thinking, using physical metaphors rather than relying solely on equations.
  • "A lot of it was descriptions in the natural physical world... a lot of it's based on intuition." ([05:12])

2. Why Google Lost the Race to Consumer AI ([06:07])

• Slowness to Commercialize:
  Despite inventing transformers, Google failed to quickly commercialize and scale them, allowing others to leapfrog with applications like ChatGPT.
  • "It's interesting why that is... what systems are in place to make that be the case?" ([06:18])

3. What Is Cohere and How Is It Different? ([07:02])

• Enterprise-First Focus:
  Cohere is building foundational language models, but distinguishes itself by unapologetically focusing on enterprise use cases, fine-tuning models for business tool integration and workflow augmentation.
  • "We're unique in our singular focus on bringing this technology to enterprise." ([07:13])

4. Data, Compute, and Algorithm Bottlenecks ([09:04], [09:32])

• Data Remains Scarce:
  Even with synthetic data, real-world, high-quality data is still a bottleneck.
• Algorithms Plateau:
  Fundamental algorithms haven’t changed much. Progress mostly depends on better data and compute rather than radical algorithmic breakthroughs.
• Compute Isn’t Everything:
  More GPUs don’t automatically translate to better models; product and application fit matters more.

5. The Hype and Limitations of Scaling Laws ([10:21], [10:47])

• Skepticism of "Scaling Laws": Nick questions the industry’s obsession with scaling (i.e., just adding more compute), using GPT-4 vs. GPT-5 as an example of diminishing returns.
  • "How much better do you think GPT-5 was than GPT-4?"
    "I actually think it was worse." (Harry, [10:50], [10:52])

6. AGI: Definitions, Hype & Industry Responsibility ([12:52], [13:00], [23:22], [56:34])

• No Shared Definition:
  Nick defines AGI as when a computer is treated as a person, but claims we are nowhere close.
• Damaging Rhetoric:
  Hype about near-term AGI (especially existential threat rhetoric) harms sober policy discussions and technological trust.
  • "I don't think Sam Altman has done a service to the world by talking about how close AGI is." ([56:34])
  • "The hype around AGI is the most damaging and confusing." ([23:22])

7. Model Specialization & The Myth of Benchmarks ([14:23], [15:08], [17:19])

• Value in Specialization:
  The best LLMs are tailored — via fine-tuning — to the context/application. Application fit outperforms generic benchmarks.
• Benchmarks Are Overrated:
  Industry, media, and consumers overemphasize benchmarks; Nick thinks they're often "gamed" and not reflective of real-world utility.
  • "They are a reflection of how much the model has been trained on those benchmarks." ([19:06])
  • "Oh, you can definitely gamify them. Yeah." ([19:12])

8. The War for AI Talent ([21:14])

• Nick Downplays Hype:
  While acknowledging some "crazy" talent headlines, Nick suggests value comes from proven impact, not sky-high bidding wars.
  • "It's really hard work. It requires a lot of experience, a lot of ingenuity, and a lot of dedication." ([21:46])

9. AI in the Workforce: Displacement, Augmentation & Inequality ([24:11], [27:21], [28:51])

• Human + Machine, Not Human vs. Machine:
  LLMs are tools to augment work, not replace core human creativity and culture (esp. in enterprise).
  • "There has been no independent breakthrough that an LLM has made. The breakthroughs are still people." ([25:12])
• Long-Term Societal Impact:
  Nick expects workforce change akin to the Industrial Revolution, with the size and shape of teams evolving, not disappearing.
  • "It will fundamentally change the way we all do work soon." ([22:40])
• Role of Policy:
  Whether AI exacerbates or alleviates inequality depends on policy, echoing the lessons of past revolutions.
  • "If there's good labor policy, I think it could help. If there's bad labor policy, it could hurt." ([28:54])

10. Open vs Closed Models & Sovereignty ([30:23], [31:27], [49:23], [52:06])

• Cohere’s Hybrid Model:
  Cohere releases weights for non-commercial research use, balancing community trust with business.
  • "That's a good sweet spot for us as a business that allows us to build credibility." ([30:35])
• Sovereignty and Geopolitics:
  Nick sees growing demand for sovereign AI infrastructure, akin to owning power plants: foundational models tailored to local language, law, and culture.
  • "Having a language model that speaks the language of your country is like building infrastructure for the people of your country." ([49:45])

11. Competing with AI Giants — Cohere’s Strategic Edge ([37:39], [38:23])

• Efficiency Over Opulence:
  Cohere’s models are optimized to run on just two GPUs, saving customers on deployment costs and sidestepping compute shortages.
  • "We have spent orders of magnitude less on creating foundational models than some of the other foundational model companies out there." ([36:02])
• Niche Focus Outmaneuvers Big Spend:
  By focusing exclusively on enterprise, Cohere doesn’t have to compete with billion-dollar consumer marketing budgets.

12. The Future of AI Interfaces ([32:49], [52:24])

• Prompting Will Fade:
  Prompt engineering as a discipline will become less relevant as models become more intuitive.
  • "I think the idea of prompting as a skill will become less relevant." ([33:12])
• Language as the Main Interface:
  Nick expects language-driven UI (voice/text) to become the norm for most business computing tasks.

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Notable Quotes & Memorable Moments

• On Sam Altman's AGI Hype:
  "AI is going to kill the whole world in two years... that was academically disingenuous and I think did a disservice to the technology he loves." — Nick Frosst ([00:00], [56:34], [56:47])
• On Specialization vs. General Models:
  "LLMs generalize really well, but they don’t generalize as well as you might think. If you want to make the best model for a given interface, it's best to be training the model on that interface." — Nick Frosst ([14:23])
• On Benchmark Culture:
  "They're a reflection of how much the model has been trained on those benchmarks." — Nick Frosst ([19:06])
• On the Human Element in Work:
  "Most of it is talking to people, understanding the culture, what's going to hit, what’s relevant. That’s not in the data set of text from the Internet." — Nick Frosst ([25:44])
• On Why Cohere Is Efficient:
  "Our model... trained to fit on two GPUs. That's a really important part of our business strategy." — Nick Frosst ([36:02])
• On Building for Legacy:
  "The idea of building something or participating in the construction of something that is bigger than you is rewarding and is fundamentally human." — Nick Frosst ([46:43])
• On AI and National Infrastructure:
  "Building a language model that speaks the language of your country is like building infrastructure for the people of your country." — Nick Frosst ([49:45])

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Important Segments & Timestamps

| Timestamp | Topic/Quote | |------------|------------------------------------------------------------------| | 05:03 | Lessons from Geoff Hinton on playful research | | 06:07 | Why Google missed consumer LLMs | | 07:13 | Cohere’s enterprise focus & model differentiation | | 09:04 | Data bottlenecks even in era of synthetic data | | 10:47 | Scaling laws skepticism: GPT-5 vs. GPT-4 | | 12:52 | Defining AGI — "a computer you treat like a person" | | 19:06 | "Reflection of how much the model has been trained on benchmarks"| | 21:46 | Talent wars, compensation, and "crazy" industry headlines | | 23:22 | AGI hype as harmful — the existential threat rhetoric | | 28:54 | Policy’s role in AI-driven inequality | | 30:35 | Cohere’s open-for-research weight release policy | | 31:52 | Competitive awareness & founder focus | | 36:02 | Two-GPU model efficiency as strategic advantage | | 37:39 | Competing with bigger-funded rivals: focus pays off | | 49:45 | AI as infrastructure; argument for model sovereignty | | 56:34 | Critique of Sam Altman’s AGI predictions |

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Flow and Tone

The episode is refreshingly candid, with Nick’s contrarian yet measured tone creating a dialogue that both challenges AI industry orthodoxy (especially around AGI, benchmarks, and value capture), and foregrounds practical, philosophical, and ethical questions. Harry’s probing style and willingness to debate adds dynamism, particularly in segments about job disruption, the value of work, and societal consequences of technological change.

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Conclusion

For listeners who missed the episode:
This conversation offers an insider’s critique of the most hyped narratives in AI, grounded in the lived realities of building — and selling — foundational models to enterprises. Nick Frosst, through examples and argument, emphasizes the importance of pragmatic, evidence-based progress, societal responsibility, and the nuanced tradeoffs involved in model development, openness, policy, and talent.

Best for:
Anyone following AI industry dynamics, model development, enterprise adoption, the future of work, or seeking a no-spin analysis of the challenges and opportunities of modern large language models.

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Transcript

[Nick Frost]

I don't think Sam Altman has done a service to the world by talking about how close AGI is. I think he has made several predictions now that are wrong and that were obviously wrong at the time he made them. AI is going to kill the whole world in two years. He did a world tour where he spoke to every major leader the world over to tell them, hey, this technology is going to pose an existential threat. And I think that was academically disingenuous and I think did a disservice to the technology he loves. A lot of the world does not think scaling laws are super prevalent.

[Harry Stebbings]

This is 20 VC with me, Harry Stebbings and today we are joined by Nick Frost, Canadian AI researcher and entrepreneur, best known as the co founder of Cohere, the enterprise focused LLM who has raised over $900 million, most recently raising a $500 million round bringing their valuation to 6.8 billion. Today we discuss how on earth they compete when competing against the billions of dollars that OpenAI and Anthropic have. Cohere has hit 100 million in enterprise ARR. And before founding Cohere, Nick was a researcher at Google Brain alongside the incredible Geoff Hinton. But before we dive into the show today, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data and the projects that we're working on across dozens of platforms, products and tools. That's why we use Coda, the All In One collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spreadsheets, Coda facilitates deeper teamwork and quicker creativity and their turn. One key AI solution, the intelligence of Coda Brain is a game changer. Powered by Grammarly, Coda is entering a new phase of innovation and expansion, aiming to redefine productivity for the AI era. Whether you're a startup looking to organize the chaos while staying nimble, or an enterprise organization looking for better alignment, Coda matches your working style. Its seamless workspace connects to hundreds of your favorite tools including Salesforce, Jira, Asana and Figma, helping your teams transform their rituals and do more faster. Head over to Coda iO20VC right now and get six months off the team plan for startups for free. That's Coda C o D A IO 20 VC and get six months off the team plan for free. Coda IO 20 VC and while Coda keeps the engine running smoothly let's talk about Brex, the ultimate financial stack for startups. So when Brex was founded, it wasn't just about creating another financial product. It was about solving the really gritty challenges that founders face daily. Let's be honest, building something from the ground up is hard enough without dealing with clunky, outdated banks that pile on fees and leave your cash idle. Brex is different. It's the financial stack that scales with you no matter where you are in your journey. From corporate cards to maximizing your Runway to earning yield on your cash. Brex was designed with founders in mind to make every dollar go further so you can focus on building. And here's what really stands out to me. Brex combines the best of checking treasury and FDIC insurance in one house account. You can send and receive money globally at lightning speed, earn Yield from day one and still access your funds whenever you need. Plus, with 20x the standard protection through program banks, your cash is not just working harder, it's working safer too. It's no surprise that 1 in 3 venture backed startups in the US with companies like Anthropic, Coinbase and Robinhood. My God, these companies are incredible. Trust Brex to help them grow. If you want to join the smartest startups on the planet, head over to brex.com startups and see what they can do for you. And speaking of incredible companies, don't forget what really keeps those customers coming back. Trust is the ultimate currency in business and today customers expect it faster than ever. And that's why over 10,000 global companies trust Vanta. Vanta automates up to 90% of the work for in demand compliance standards like SOP2, ISO 27001 and more. Using smart AI to centralize workflows, manage risk and get you audit ready in weeks, not months so you can stop chasing paperwork and closing deals. And a new IDC report found that Vanta customers achieve $535,000 per year in benefits. That's insane. And the platform pays for itself in three months. I had no idea about these. Whether you're growing fast or just getting started, Vanta connects you with trusted auditors and experts support to help you build trust with customers. Get $1,000 off your first [e-mail] 20VC. That's vanta.com 20. You have now arrived at your destination.

[Interviewer (Harry Stebbings)]

Nick. I'm so excited for this dude. When I had Aidan on the show he was like, you've got to have Nick on.

[Harry Stebbings]

He's the real star of the show.

[Interviewer (Harry Stebbings)]

And he introduced us way back then. So I'm so excited that we can make this happen.

[Nick Frost]

Yeah, man, I'm happy to be here now.

[Interviewer (Harry Stebbings)]

Before we dive into cohere, I have.

[Harry Stebbings]

To ask, you were Geoff Hinton's first.

[Interviewer (Harry Stebbings)]

Hire at Google Brain, and so then you're put in a room with Jeff Hinton. You get to work with him every day. What was the biggest lesson from working with Jeff? A legend of the industry?

[Nick Frost]

Yeah, I learned. Yeah, I loved working with Jeff. I learned everything I know about research from those. I think we were there for four years, three years. I was very surprised at how creatively and playfully he approaches research. When we would discuss, like, algorithms or optimizers or loss functions, we would discuss them through physical analogy. So we'd spend a lot of time talking about, like, imagine there's like a ball here and like an elastic band to this thing and a pulley here. And like, this is what the. You know, it's on this kind of a surface. And like, a lot of it was descriptions in the natural physical world. And that was very, yeah, like, playful. And a lot of it was approached with, like, oh, what would happen if, you know, with curiosity. And I didn't, when working with him, I didn't expect that. I expected it to be much more like, you know, just, here's the equation. Let's. Let's figure out what the derivative is and let's. Let's go from there. Whereas instead, a lot of it's based on, like, intuition.

[Interviewer (Harry Stebbings)]

When you look at Google Brain and you look at DeepMind a lot think that really kind of Google were asleep at the wheel, given them not being at the forefront in what was the consumerization of it with ChatGPT. Do you think that's fair?

[Nick Frost]

I don't know. I mean, it's certainly interesting. Look, like the transformer was invented at Google, right? Like there was in 2017, Aidan, amongst many other brilliant people in Google Brain, published the Transformer as an architecture. It wasn't then commercialized very quickly within Google. It wasn't scaled up very quickly within Google. A lot of that work had to be done elsewhere and years later. So that's interesting why that is, like, what systems are in place to make that be the case? I don't know. I will say there's still a ton of brilliant people in DeepMind. I think now that's just subsumed the rest of it, doing great work and they continue to make good products. It is interesting that all the people who worked on the transformer left to continue to work on the transformer for.

[Interviewer (Harry Stebbings)]

People who don't know, and just to set the scene before we dive in properly. What is cohere and how does it differentiate from more generalized models that are maybe more well known, like your OpenAI's and your Anthropics?

[Nick Frost]

Yeah, so we're a foundational model company like those other two. So we build foundational models, we build language models. I don't know, Maybe there's like 10 companies in the world that are building large language models.

[Interviewer (Harry Stebbings)]

In the west, there's a few that.

[Nick Frost]

Have popped up recently, some number less than 20 in the whole world. Most of them in America, a handful of them in China, US in Canada, and one in France. So those are really the companies out there. We're unique in our singular focus on bringing this technology to enterprise. We train a model that is good at enterprise tool use. So like, we train a model that you can give it a bunch of tools and APIs within your business, give it access to your business's data, and then you can ask it to help you with something in your work and it does a good job of it. So that's what we train it for.

[Interviewer (Harry Stebbings)]

How does a focus on enterprise over consumer change the way in which you train and build a model?

[Nick Frost]

Yeah, so the models themselves, like transformer architecture, which is the original model that. Yeah, that was introduced in 2017, hasn't changed very much. The whole industry is still using transformers. We've changed the way we train them. But the model architecture itself, you know, we're approaching 10 years of the same model architecture. When we train our model, we're not training it to be like an amazing conversationalist with you. We're not training it to keep you interested and keep you engaged and occupied. We don't have engagement metrics or things like that. We're just training it to augment you in the workplace. We're just training it to help you do your job. And that means the type of data we train it on is very different. So recently we started doing a bunch on synthetic data, generate a whole bunch of data to create fake companies and fake emails between people at these fake companies and fake APIs within those fake companies. And then we train the model in that synthetic environment to help out within that fake business.

[Interviewer (Harry Stebbings)]

Do you think data is a bottleneck given the ability for synthetic data to produce infinite supply?

[Nick Frost]

Yeah. Data is still a bottleneck. You need real world data in order to start a process of synthetic data. Synthetic data has helped a lot and it's made models better than they would be if they didn't have access to it. But getting access to high quality data is still something people think about. We still make a whole bunch of data in house with annotators who are making real data and not synthetic data.

[Interviewer (Harry Stebbings)]

When you think about kind of the three pillars of compute compute, algorithms and data, which one do you think is most constrained or the biggest bottleneck?

[Nick Frost]

It's interesting. I mean, the algorithms haven't changed very much. They've changed a little bit. You know, when we started this industry originally we were just training base models, which are not called base models at the time. They were just called large language models. But they weren't trained from human feedback. So all they would do is take in the first part of a sentence and write the second part of a sentence. But if you tried to have a conversation with them, it wouldn't work because that wasn't the data they were trained on since then. Now we train models in a few different steps. There's like a base modeling step, then there's a reinforcement learning step from human feedback with SFT data. After that, there's a variety of other reinforcement learning techniques you can do. But the algorithms I think are not the bottleneck in terms of making those models more useful. A lot of it is still getting good quality data and then making good quality synthetic data from your good quality real data.

[Interviewer (Harry Stebbings)]

When we think about the bottlenecks, that leads to potentially a plateauing that people are worried about. And everyone seems to now be on the train of hey, more compute. Scaling laws are more real than ever and we will continue this exponential progress with more compute. Do you agree that we are seeing the benefits of scaling laws for the continuous next 12 to 24 months? Or do you think that actually more compute will not just lead to more progress?

[Nick Frost]

Well, how much better do you think GPT5 was than GPT4?

[Interviewer (Harry Stebbings)]

I actually think it was worse.

[Nick Frost]

Tells you something about the nature of just throwing more compute at the problem.

[Harry Stebbings]

Does it? Or does that show?

[Interviewer (Harry Stebbings)]

And so why do I think it was worse? I think it was worse because actually the way that they now do my model selection is slower and more cumbersome. And actually it's a pain. It gets it wrong sometimes. For me, I just want a quick answer and it suddenly goes into deep research. I'm like, oh for fuck's sake, I just want a quick answer. Alright, PhD, calm down. Do you know what I mean? And so I think it's a worse product in that respect. And I think we waited for a year or a Year and a half for model auto selection.

[Nick Frost]

I think if I go back to your original question of do I think just throwing more compute, some people are thinking there's a plateau? Do I think there's more compute? I think we need to agree on where we think the technology is going to establish whether or not there's a plateau. Language models are incredible. I use them in my work life as often as possible. One of the reasons why we're focused on the enterprise is because that's really where I think large language models are useful. Like, if I look at my personal life, there's not, there's not a ton that I want to automate. You know, like I actually don't want to respond to text messages from my mom faster. I want to do it more often. But like I want to be writing those, I want to be like engaged, you know. Whereas in my work life there's a ton of stuff I don't want to do. Like we need to get to a stage where I can, you know, open up north and I can say, hey, file my expenses. And then it can figure out, okay, cool. I got to, you know, look through all your emails, I got to look through photos of receipts you've taken. I got to cross reference that with the things you're allowed to expense via internal documentation. Then I got to figure out what the API is for, how to expense things within your company. And then I got to do all of those and get approval before I do them. Like that's a super, that's a many step process, but that's where the technology is going. That work, the work of making model do that is not plateauing, that's more modeling work, that's more product work. That's like building better connectors, that's building safer data integration so that you can trust giving a model access to the types of stuff I just said that stuff's still ongoing and that's what we're working on. I think when people are talking about building towards AGI, like I don't think this technology gets us there.

[Interviewer (Harry Stebbings)]

When you say gets us there, what is there?

[Nick Frost]

Well, yeah, great question. We've had many years of people discussing AGI and not many definitions thereof.

[Interviewer (Harry Stebbings)]

Next to none. I mean, my definition is when Sam Altman and Microsoft decide, yeah, they've changed.

[Nick Frost]

Their definition a few times on that. When I say AGI, what I mean is a computer that you treat like a person, when you use a computer and you expect it to behave like a person and treat it that way, I'll Call that AGI?

[Interviewer (Harry Stebbings)]

Do you not think we're already there then?

[Nick Frost]

People do not treat language models like they treat people.

[Interviewer (Harry Stebbings)]

Do you think OpenAI and Sam Altman then now realize that more compute does not lead to this exponential progress when they look at GPT5?

[Nick Frost]

I don't know. Yeah, I don't know. I think they're a great company. They build a really cool consumer product.

[Interviewer (Harry Stebbings)]

Why does the world still think scaling laws are so prevalent when you don't?

[Nick Frost]

A lot of the world does not think scaling laws are super prevalent. If you go out into a university and talk to the students there who are studying computer science, or even the students who are not, and you ask them like, hey, is throwing more compute at this problem going to get us to AGI? Most of them say no.

[Harry Stebbings]

You mentioned there are a couple of.

[Interviewer (Harry Stebbings)]

Different use cases in terms of expense management. Was one that you clearly articulated. A question that I think I have and a lot of people have is how far do models go in terms of value capture into the application layer? And you're seeing anthropic now with Claude really challenge the cursor of the world. You're seeing OpenAI with a lot of consumers, human products, you with a lot of enterprise use cases. How do you think about whether they stay as AWS style commodity layers or whether they extend into value capture of application layer?

[Nick Frost]

Yeah, that's a good question. I don't see the two as that different. I see the two as related. And if you want to be making a good product with a large language model, you are best suited training that large language model for that product. That's one of the really interesting things about LLMs is that they're really phenomenal. They generalize really well, but they don't generalize as well as you might think. And if you want to make the best model for a given interface, it's best to be training the model on that interface. So I think the two things are more related.

[Interviewer (Harry Stebbings)]

So do we see this deeply specialized, unbundled model world where you have exactly that Very, very specific use cases where models are trained for or N11 labs of the world would be another brilliant use case with specifically voice. Is that the world that we live.

[Nick Frost]

In, there's like a spectrum, right? Like the old world of machine learning back in the old world back in 2015 or something.

[Interviewer (Harry Stebbings)]

Oh my gosh.

[Nick Frost]

When the world was new, any task you wanted to do with a neural net, the best neural net you were going to get was training a model on that task. So if you wanted to make a task that was going to like a neural net that was going to identify pictures of cats and tell you how many cats were in an image. You were best suited to train a model on identifying pictures of cats, that was like the best. And the world of machine learning and the first half of that decade was all about, here's a problem. Make a data set, train a new model from scratch, or maybe take like sift features or maybe take whatever, some base model, but pretty much fine tune like train a model on the data set itself and go to production with that model. That's not the case with language. If you want to make a model that's the best at summarization, you can't just train it on summarization. You have to train it on all language. That is the technological reality that has brought us to where we are today. And that's true for foundational models and not true for the neural nets of 2015 and before. That's super interesting. But that's like a spectrum. On one end is every single task, train a single model for it. On the other end is train a model, one model to do everything. I think the reality of what we're seeing with Transformers is they're not at this end of the spectrum. They're like a little over here and they're like, train a model that is generally good at all language and refine it on the type of stuff you want to do with it. So you're seeing that like anthropic code models, very good at code. But they didn't train a model specifically, like a refactoring model or a debugging model or they didn't train a model just for writing test cases. It's a model that is good generically at code for us, for cohere with our focus on enterprise and like secure deployments and customizations for our customers. That means training a model that is good at helping people in an enterprise setting. So, like using internal tools, reading through massive amounts of documentation, understanding, does that.

[Interviewer (Harry Stebbings)]

Mean the model doesn't need to be as good? Again, I've learned to be incredibly blunt. If someone wants to criticize, they say, oh, well, if you look at models or evals, cohere is not as good.

[Nick Frost]

Well, there's a whole long conversation to be had about evals. But effectively, like, you know, what we care about is not discord, not the hype, not the discourse. Like, what we care about is if a customer uses our model and they try to do something with it, we care that it works. As easy as possible. Like, that's what we optimize for. None of those are really reflected in the various benchmarks that cycle through every year. And so we don't, like, focus too much on that stuff.

[Interviewer (Harry Stebbings)]

Do you think the benchmarks are bullshit? Because we place a lot of emphasis on them on Twitter sphere, on the Reddit sphere. Are they bullshit or are they accurate? Reflection of model progress.

[Nick Frost]

Let's go back in time a little bit. When we first started in this industry, the benchmark that was used the Most was called LM1B. That was a benchmark that was like taking in the first part of text, like of a. Of a newspaper, and then writing the second part of a. Of the newspaper article. After that, there was a benchmark called hello Swag. Do you remember that one?

[Interviewer (Harry Stebbings)]

I do remember that one.

[Nick Frost]

Cool. Okay. So that's like 2022. So that's like.

[Interviewer (Harry Stebbings)]

That's my introduction.

[Nick Frost]

No one's talking about that anymore. Right now a lot of people talk about, like, AIM as like a math reasoning Amy or actually don't Math reasoning benchmark. None of our customers ask the model to do math reasoning. That doesn't come up in the workplace that often. That comes up in a few workplaces where mathematicians work, but there aren't a ton of people out there making a living doing math reasoning stuff like the ARC AGI challenge is a benchmark that people talk about, but that's like a pixel manipulation challenge. It's like taking in, like, a grid of pixels based on rules, predicting the next one. That's not a thing any of our customers have ever asked the model to do. So do I think they're all bullshit? It's interesting. I don't know. There's good scientific work in some of them. I think it's very interesting to evaluate emergent capabilities from models, but they're not.

[Interviewer (Harry Stebbings)]

An accurate reflection of the utility value of models.

[Nick Frost]

They're a reflection of how much the model have been trained on those benchmarks.

[Interviewer (Harry Stebbings)]

So you can gamify them, essentially.

[Nick Frost]

Oh, you can definitely gamify them. Yeah.

[Interviewer (Harry Stebbings)]

Do the big players gamify them?

[Nick Frost]

I don't think those leaderboards are that helpful. I think in a consumer space, it's cool. I think if you're making a consumer app and it's, like, exciting and fun and people like to look at it and they want to try out the most recent thing, that's fun. That's cool.

[Interviewer (Harry Stebbings)]

Given the pace of deployment, we are seeing model evolution so fast and so rapidly that you're essentially seeing this Kind of decay rate on models being greater than ever because it's like, next one, next one, next one. And actually they're still being trained, though, on H1 hundreds or Nvidia chips from 18 months ago. Is there a misalignment in terms of the progression of models versus the progression of chips?

[Nick Frost]

You can cycle through new versions of models quicker. I mean, it still is very slow still. When I was training neural nets in 20, I don't know, 11, and it would take like hours to days. I remember being like, this is crazy. I can't believe this takes so long to train this model. Now we spend months, months training models. So, like, you know, that's, that's a timescale I didn't anticipate when I was working on this a long time, when I was working on neural nets a long time ago. But that's still very different than the timescale of working on chips. Right? Like, that's still slow, I think when you talk about, like, we're seeing all these models iterate so quickly. Like, yes, on the one hand, we're seeing models iterate really quickly and people are releasing new models. On the other hand, there's still the transformer that was invented in 2017, and they're still sequence models and they still take in words and predict the next word. And we've changed how they're trained a bit. We've added on steps. Like now there's a base modeling step, then a sft, like supervised fine tuning from human feedback where like somebody writes a sentence and then writes the response they want and we train on that. And then there's a reinforcement learning aspect where the model is generating and you're telling it that's good, that's bad, or something. So there's like new ways of training it. But fundamentally the tech is quite still the same. We keep making them better, keep iterating on them. But it's not as though we've, like anybody has trained a model that's fundamentally different than a transformer. It's an interesting dichotomy. On the one hand, there's constantly new stuff. On the other hand, we've been working on the same stuff for a while.

[Interviewer (Harry Stebbings)]

We have been working on the same stuff for a while. The thing that has seemingly changed is the value of the people working on the stuff. We're now seeing billion dollar people in terms of Zuck's willingness to pay for chief scientists. Joel Pinault.

[Nick Frost]

Yeah, Joel.

[Interviewer (Harry Stebbings)]

Yeah, Joel from Facebook Meta. My question to you is, how do you think about the war for talent that we're seeing today.

[Nick Frost]

I think there's a lot of crazy headlines out there. I don't know how much of it is real.

[Interviewer (Harry Stebbings)]

I know you don't think it's real that anthropic are paying 10, 15, $20 million for great AI researchers?

[Nick Frost]

I have no idea. I know that there are lots of people who are adding that much value and there's lots of people who are like, bringing that much value into the industry. It's a super impactful industry. So I know that there are people that are bringing that much value and I know there's lots of brilliant people. And I know that it's really demanding work. It's really hard work. It requires a lot of experience, a lot of ingenuity and a lot of dedication. It's a good place for people to be spending their time, and I think it makes sense that many of them are rewarded very well. That being said, when I see the stories of meta hiring people for 100 million, like, I read as many stories of those as I read of people leaving the next day. So I don't. I don't know what's going on over there, but when did you spend 5.

[Interviewer (Harry Stebbings)]

Million on an AI researcher?

[Nick Frost]

There are certainly lots of people who, through our equity, own what you're talking about.

[Interviewer (Harry Stebbings)]

Do you worry that the industry is kind of becoming commoditized or transactionalized with the hype around it?

[Nick Frost]

I don't like. Yeah, I do think the hype around it is misleading sometimes. Like the technology. Like, I'm in such a strange place of being caught between. The technology is the most beautiful technology I've ever seen. It's the most transformative technology I've ever worked with. It is already fundamentally changing the way I do work, and I'm very sure it will fundamentally change the way we all do work soon. On the other hand, there's a lot of hype around it. There's a lot of misleading rhetoric, there's a lot of misinformation, and I don't think the hype is necessarily helpful for getting to the truth.

[Interviewer (Harry Stebbings)]

Can I just dig in there? What do you think is the hype and misleading rhetoric that is most damaging or confusing?

[Nick Frost]

Yeah, I think the hype around AGI is the most damaging and confusing.

[Interviewer (Harry Stebbings)]

This assumption that we will all have no work to do, we'll all be in ubi.

[Nick Frost]

Yeah, this isn't really in the discourse as much this year as it was last year and the year before that. And that's because it's pretty clearly not true. But the idea that, oh, this technology poses an imminent existential threat to humanity was incorrect and not helpful for talking about the real ways in which this technology could be damaging. The real ways in which this technology could shake up a system and cause rapid changes. It was not helpful for getting people to understand what the technology is. So I don't hear that as much anymore these days. I think that's because people have realized that that's not the case. But the remnants of that discourse are still in the world, are still out there.

[Interviewer (Harry Stebbings)]

I think the remnants are, and I think they're most prevalent in the way the internal employees in large organizations respond to AI being introduced. People do not welcome the introduction of AI in large companies. Maybe Europeans aren't, but a lot are very nervous and scared and do not embrace it wholeheartedly.

[Nick Frost]

Yeah, I think I haven't found that as much when we've worked with our customers. I find a lot of people are interested and excited about using an LLM. Mostly that's because I think they realize that the LLM is augmentative for the most part and it allows them to not do the things they don't want to do. The artistic.

[Interviewer (Harry Stebbings)]

I mean this in a nice way.

[Harry Stebbings]

Do you actually buy that?

[Nick Frost]

Do I actually buy.

[Interviewer (Harry Stebbings)]

Yeah, like nice. Yeah. I have Benioff on the show from Salesforce two days ago and he says like, oh, the same human plus agent.

[Nick Frost]

Yeah.

[Interviewer (Harry Stebbings)]

You serious? Most 25, 26 year old marketing managers or SDRs, I'm sorry to say it, they are not brilliant. They do not love the craft. They are not better than a phenomenal agent will be in the next 12 months. They will be replaced.

[Nick Frost]

Oh, no. Yeah, I actually believe it. Yeah, no, I believe what I said. Sorry. Yeah, Yeah. I fundamentally believe that this technology, that there are things that it's way better at you, better than you at. But there are still lots of things that people are better at. Like look, LLMs are incredible people. People have been using them, you know, for years now. There has been no independent breakthrough that an LLM has made. Nobody has seen, hey, nobody asked an LLM solve this problem no one's solved before and get the answer. The breakthroughs are still people.

[Interviewer (Harry Stebbings)]

Is that not only a matter of time?

[Nick Frost]

No, that's not a matter of time. That's fundamentally the way that sequence models work. Like we are training statistical models of text. They're phenomenal. They are capable of generalizing across unseen tasks, which is why they're so useful. But when you're talking about like the 25 year old marketer or something. Some portion of their work is like writing text on a computer. It's like all the information's out there. They have this document and that document, this tool, that API. They just need to take that and turn it into another form and like, you know, combine it and then put it out there. Like that's the work. That's some portion of it, it that's not the majority of it. Most of it is talking to people like understanding the culture, understanding the zeitgeist, understanding like what's going to hit what's relevant. Using their intuition and their human experience to understand how they can be helpful at what they can do. And that is not in the data set of text from the Internet.

[Interviewer (Harry Stebbings)]

I disagree. Because you know what they do now? They go, hey, I'm running a campaign for Evian, come up with three different storylines that would be cool for us to do. Make it relevant to news cycles today is the prompt. And then they come up with three and they're like, oh, that one's pretty good.

[Nick Frost]

And I think that sounds like a good usage of it as a starting point. And then I'm sure it is worked on and some of those are thrown out because of things they understand. And some of them are like, oh, that's a great insight. I'm going to run with that. I'm going to tinker that like what you just described is a good use case and I can imagine as a jumping off point that would be helpful. But that's not where the work ends. Like that's the beginning of the work you've just augmented, you're now starting with, instead of a blank page, you're starting with things to go for.

[Interviewer (Harry Stebbings)]

So you don't buy that we're going to see this dramatic reduction in team sizes?

[Nick Frost]

I think we're going to see changes to the nature, like to the workforce in the same way that we saw changes to the workforce when the computer was created, when the personal computer, when the Internet was created, when the printing press happened. Right. Like we've seen like in the Industrial Revolution. Right. Like we've seen drastic changes to the work workforce and that's going to keep happening.

[Interviewer (Harry Stebbings)]

What will the changes be like, what will the company look like, do you think, in five to 10 years.

[Nick Frost]

That's an interesting question. I think you will arrive to work and you will sit in front of a computer and you will predominantly use language to interact with that computer. Anytime there's something to do that you know can be done and you know, like the information is out there, doesn't require creativity or insight. You know that it's there, you just need to do it. And doing it's kind of boring. You will get the model to do it for you. That mostly looks like sitting down, speaking to the computer, getting it to do the things you don't want to do, and then spending your time talking to other people, thinking about how it can be useful, whether or not what it did was good. I think that shift is maybe chaotic. And I would like us to be spending time thinking about how can we make sure that changes as easy as possible. How can we make sure that making language models allows people to do the stuff that they're good at and that they like? How can we make sure the labor force is resilient? How can we make sure that income inequality doesn't go up as a result of that? Those are the types of things I would like us to be talking about, and those are the important things. To go back to your earlier question when talking about the risks of AGI, I think those existential threat questions made it harder to talk about the real things like income inequality.

[Interviewer (Harry Stebbings)]

Do you think AI does more to help or to hurt income inequality?

[Nick Frost]

I think it depends on policy. I think if there's good labor policy, I think it could help. I think if there's bad labor policy, it could hurt.

[Interviewer (Harry Stebbings)]

Can you explain that to me?

[Nick Frost]

Look, when you saw the last Industrial revolution, broadly speaking, everybody looks back on that Industrial Revolution and says that was a good idea. Like nobody's saying, hey, we shouldn't have automated what was before the Industrial Revolution. It was like, I don't know, 90 something percent people working in farms. Now it's like 5 less than that. Everybody thinks that was a good idea. It was a crazy time. And if you read stories about what went on during that moment, there's lots of things that people did that they stopped doing pretty quick, like having kids work in coal mines. That was a crazy thing. And out of that Industrial revolution came a whole bunch of really good labor policies came. Unions, workers rights, things that I think we also think were a good idea and resulted in not only, you know, better lives for people, but actually more productivity, like actually a better economy, actually, you know, a better world. A lot of those were from public policy. A lot of those were from things created in unison between businesses and governments.

[Interviewer (Harry Stebbings)]

Why do we need to have such significant policy change if it only augments humans and it doesn't replace them?

[Nick Frost]

Right now we've seen income inequality go up over the past several years. And a lot of that was happening before AI, before language models were popular. And I'm worried that technology has the potential to exacerbate that without being deployed correctly and without having good policy around employment.

[Interviewer (Harry Stebbings)]

Can I ask you, when we think about problems to solve, I think a.

[Harry Stebbings]

Lot of people also get worried about.

[Interviewer (Harry Stebbings)]

The open versus closed argument. How do you feel about where the future of efficient AI lands in the balance between open versus closed models?

[Nick Frost]

So at cohere we make our foundational models and then we release the weights for non commercial usage. So we're somewhere in the, between the, like open and closed, right? We're a for profit company. Like we, we exist to make money, we release our weights for scientific and research and like, you know, you can download it on your computer and run it. That's a good sweet spot for us as a business that allows us to like, you know, build credibility within the community. If people want to check out our weights, like they can go check them out, right? Like there's lots of companies that started out as open who no longer release the weights of their models or who never did, right? So we have our models out there. You can go look at them, you can use them, you can validate, hey, do they work on my problem, yes or no. But if you're using them for commercial purposes, you got to talk to us and then we figure out a commercial relationship so that we can exist as a business that works for us. I'm surprised there aren't more businesses taking that tact, more foundational models taking that approach.

[Interviewer (Harry Stebbings)]

Do you think matter will move to a closed model approach from an open.

[Nick Frost]

They've certainly hinted at that right there certainly looks like, but I don't know what they're, I don't know what they're doing over there. I don't, I don't think a lot of people know what they're doing over there and I don't spend a lot of time thinking about it.

[Interviewer (Harry Stebbings)]

Do you not think it's helpful for founders to be very aware of competitive landscapes in case they're asked about them by customers? In case customers are going, hey, why aren't you more open? Why aren't you more closed? Is our data secure?

[Nick Frost]

If you're, as with most things, like a middle ground is the right place to be, right? You could spend your whole time as a founder only looking at competitors and being like, oh, why are they doing that? Why are they doing this? What's going on with that? And that will, I think, not be Helpful for you. You could also spend your whole time with your head in the sand only thinking about what's going on in your company. And I think that would not be helpful either. You have to find some middle ground. The discourse around AI is inescapable. You would be hard pressed to ignore it. It is every other headline. I don't think there are many people who work in the industry who suffer from not enough information about what's going on in AI. Right. I think there's a lot of people who suffer from way too much of it and obsessing over the minute details of like so and so got 0.2% better on this thing. Or like, you know, is constant small changes in businesses out there. And I think that can mislead you from staying grounded. What are you actually doing? Who are you actually helping? How is this making you know things better for your customers?

[Interviewer (Harry Stebbings)]

Do you think we will still have prompting as to call user input guidance mechanism in five years time?

[Nick Frost]

Prompting as in like you write something to a model and it writes back. Yeah, yeah, yeah, what else would it be?

[Interviewer (Harry Stebbings)]

The way that it changes, the way that you do it changes. You wouldn't say, hey, make it a funny tone or hey, add in a light personalized style that also is sincere.

[Nick Frost]

I think the idea of prompting as a skill will become less relevant. And if you look at like that's the trajectory, like when I started doing this, if you wanted to get a model to summarize something, you wrote the first paragraph and then you wrote in summary, colon, new line and then you generate it. That was the skill of prompting was figuring out how to trick a model into getting it to do what you wanted to do. And that's because they weren't trained on feedback from people. They were only trained on text from the web. And so all they were were sequence models from text on the web. And nobody on the rep on the web wrote please summarize this for me. And then a summary. They wrote a paragraph and then wrote in summary colon. And then so if you wanted to get the model to do that, that's what you would do. Language models are like, we're training them more to fit how people expect them to work. And that means that getting good at prompting is less important. So I think the idea of saying like oh yeah, you got to learn how to prompt is going to go away. I think the idea of saying you need to learn how language models work and you need to know what they can and can't do in the same way you had to learn how a computer works and what it can and can't do, you have to learn how a telephone works and what it can and can't do. Like, I think that's going to exist and that means, like. Like prompting is going to exist. The idea that you write something to a model or you say something to a model and then you get the response back, and if it's not what you like, maybe you iterate a little bit. Like that's going to exist. That's fundamentally how the technology works. But the idea of it being a discipline that you have to train to do, we've already seen that trajectory. Yeah, it's already gotten easier. I look for people who know how a language model works. One of the things that's been a necessary component about working echo here is you can't think the technology is magic. You can't think. This is like we're doing spells. You have to know how a language model works, how it's trained and what that means for it, what emergent capabilities happen, which ones don't. You can't think, oh, yeah, I just ask the digital God to do my work and then it does. That's not what the technology is. And thinking that will not help you build it and it will not help you use it.

[Interviewer (Harry Stebbings)]

If we hone in a little bit more on you, you led or a large part of the latest fundraisers. We were chatting before. When you think about the fundraising journey, how was that journey and are there any big lessons from. It was 600 million?

[Nick Frost]

No. Yeah. I actually quite. I quite like talking to VCs and to pension funds and to people. I think it's. I love cohere, I love what we build. And I also like talking, so I.

[Interviewer (Harry Stebbings)]

Like talking about both functions very similar.

[Nick Frost]

Oh, between them. Yeah. Actually, that's an interesting question. Yeah. I do think, look, the industry is a lot more matured. Two years ago, you know, when we were fundraising or three years ago, a lot of the questions were like, what is this? Like, how are you gonna. What. How does that work? What is this? You know? And so we'd spend more time explaining stuff. People mostly know how it works and know what it does. And now we can say, here's what we're doing for our customers, specifically, you know, like, here's how RBC is using it, here's what we're doing with Fujitsu, here's what LG is doing with it, you know, like, we can talk about those things specifically. That's more interesting.

[Interviewer (Harry Stebbings)]

How much of the 600 million will be spent on computer.

[Nick Frost]

Yeah. There's like three components that go into making language models, right? There's talent, there's, like, people, smart engineers and researchers. There's compute and there's data. The importance of those has shifted and the spend of those has shifted over time. We train very efficiently. We train efficient models. So like our model command A and the command A reasoning model, which we just released those, they're all trained to fit on two GPUs. That's like a really important part of our business strategy. It turns out if you go talk to a lot of companies who wanted to deploy models into production, they were bottlenecked on, on deploying because they don't have enough GPUs. Two GPUs turns out to be like a sweet spot between performance and cost and like, and actually how many GPUs they had access to. So that means we train very efficiently as well. We have spent orders of magnitude less on creating foundational models than some of the other foundational model companies out there. Truly orders of magnitude less. And I'm very proud of the efficiency of the team and, like, what they've done with the, you know, the resources that they have. We think about efficiency a lot for ourselves and for our customers, and those two things are related. But how much of our funding goes to compute? It shifts over the years. But a lot compute is.

[Interviewer (Harry Stebbings)]

How has it shifted over the years?

[Nick Frost]

I mean, when we first started cohere, one of the very first things we did because we had no funding was we spent next to nothing on compute. And we showed that you could train a model by having, like, a bit of a GPU over here and a bit of GPU over here, a bit of a GPU over here, and you could link them together. And we published a few papers on that, on training models with, like, the scraps of GPUs in data centers. Right. That was what we started with. And we showed that you could do that, you can do that. It's very slow, and it's much easier to just rent a big data center and train the model there.

[Interviewer (Harry Stebbings)]

The question that everyone asks is, how do you compete against competitors who have billions and billions of dollars? Do you hate that question? And how do you respond?

[Nick Frost]

No, I don't hate that question. I think, yeah, I think that's a fine question. We've announced funding rounds. They are smaller than some of the other funding rounds out there. We're pretty singularly focused in a way that the other companies who build foundational models are not right. Like we don't have a consumer app. We're not trying to get anybody to spend $200 a month on something for their personal lives. We're singularly focused on working with enterprises and businesses, making sure that they get to production with AI. My whole I'm constantly telling people like not AGI, roi, roi, not AGI. There's a lot of work that still needs to get done there using something.

[Interviewer (Harry Stebbings)]

Do you think then the OpenAI and Anthropic will just seed enterprise?

[Nick Frost]

I don't know. I think right now both those companies are pretty cool. They've both made good consumer products. Where this technology adds the most value is in work for personal reasons. That's where I see this technology being the most useful. I don't know if they'll start working on that. I know that that making models that work in that environment is pretty different than making a model that works in a consumer environment. In a consumer environment, you can make the biggest model possible. You can have complicated switches to tell you to go to this model or that model because you're just posting it on a huge amount of GPUs. You can be losing a ton of money on every inference call. But you're getting users and something and so that works. The types of models you have to build to succeed there are different. I know the work you need to do on the interface. We we've announced north, which is our agentic framework. It's privately deployable, customizable for knowledge workers within an enterprise. It looks pretty different than some of the consumer applications. Right? Like a big one is like our models and generate images. Nobody in a workforce is really wanting to generate images as part of their work. But as a consumer it's very fun. It's very cool to be like, oh, here, give me a picture of this or something. So the types of models we train are different and the interfaces we make are different. I don't know if they'll be interested in that at some point. I think we stay focused on talking to customers and adding value. How do you price entirely depending on what the customer wants to do with us. So we do have some customers where we give like make a custom model for them and give them that model.

[Interviewer (Harry Stebbings)]

So do you have forward deployed engineers?

[Nick Frost]

We do, yeah. So they're a crucial component of how we like go get a company up and running and into production with us.

[Interviewer (Harry Stebbings)]

Do you think everyone will have forward deployed engineers in a future AI world? World in a way that Palantir has.

[Nick Frost]

Glamorized Yeah, I mean, I think Ford deployed engineers are a good idea. Right. Like you're selling technology to somebody. It makes sense to have some engineers who come and help them get it set up and work with them to, like, make sure it's actually delivering value. You know, I think that's a good idea. I don't know if that's true for every business.

[Interviewer (Harry Stebbings)]

Does FDEs not just allow for poorer technology?

[Nick Frost]

No, no. I think that there's this. There's this idea sometimes, like, oh, yeah, you can just make the thing and for every business, it'll work perfectly and require no engagement. That's the way some technology works. That's the way a lot of consumer technology works. That's not the way a lot of enterprise technology works. Right. Like you're selling things to people that have to be, like, matched to the way their business is set up. And so having engineers go along with it and say, cool, here's the model, here's what we can do to make sure that that's perfect for you in your specific use case is helpful given.

[Interviewer (Harry Stebbings)]

That you sell to enterprises. I'm an enterprise investor and I love enterprise. Revenue quality is much higher, much stickier. Growth is slower because you're working with large enterprises. Do you think you have an enterprise discount applied to valuation because of revenue growth being slower because of enterprise?

[Nick Frost]

Such a question.

[Interviewer (Harry Stebbings)]

What was the price on the last round? It was public. I think it was like 6.78.

[Nick Frost]

6.8, yeah. These are all staggering numbers. These are all numbers that are impossible for an individual to conceive of. We're so far into that, you know, as a single individual, this is well beyond the realm of what you can reasonably engage with in your life. So, yeah, for a regular person who grew up working as a cook. Yeah, like, my first job was burgers. Like. Yeah, yeah, that's a great. You know, these are all crazy numbers.

[Interviewer (Harry Stebbings)]

Do you care about money?

[Nick Frost]

Yeah, I think. Yeah, certainly. I think everybody cares about money and everybody's motivated by money.

[Interviewer (Harry Stebbings)]

With being motivated by money, even you're an incredibly acquisitive asset. And it's been a very strategically important thing for large players to do. Have you had M and A offers across the journey?

[Nick Frost]

Oh, yeah, we have at times. Yeah.

[Interviewer (Harry Stebbings)]

How's the decision making gone there? I always want to be in the room. I always picture it kind of like thundery nights.

[Nick Frost]

No.

[Interviewer (Harry Stebbings)]

And people coming together where it's raining outside.

[Nick Frost]

Oh, no, no, no, no. Yeah, we have. I mean, we. Look, we've been at company for five years. Yeah, we have.

[Interviewer (Harry Stebbings)]

Not tempting.

[Nick Frost]

We're all being like the co founders and now the people who work here. A lot of things you'll hear Aiden say is like, oh, building a generational company. We're all really interested in building something that outlasts us and that goes beyond our involvement of it. That's really exciting.

[Interviewer (Harry Stebbings)]

Why do you want that? I know it sounds strange. Why do you want something that outlasts you?

[Nick Frost]

Oh, yeah, that's. Remember earlier and I was like, oh, sometimes I can ask philosophical questions and then we deviate too far off the thing and this is one of those questions.

[Interviewer (Harry Stebbings)]

No, it's cool. I love this.

[Nick Frost]

Yeah.

[Harry Stebbings]

This is why though, people say like.

[Interviewer (Harry Stebbings)]

Oh, AI could just replace you as an interviewer, Harry. It's like, no, it couldn't because it doesn't have the ambiguity to go off on the like. But why does that actually matter?

[Nick Frost]

Well then if you think what you just said, why do you not think that that exists for all jobs?

[Interviewer (Harry Stebbings)]

Oh, because I think what I do is a very disciplined art honed over 10 years compared to a social media manager who's 24, coming out of university writing with thrilled to publish our latest report.

[Nick Frost]

That's. Everybody thinks that what they do is honed and trained.

[Interviewer (Harry Stebbings)]

And why is mine paid millions and theirs not? Because society places strategically more value on mine. If we're being a dick and blunt, and I'll take this out, because very few people can do it.

[Nick Frost]

There's labor that's easier to work, that you can learn faster or like you can get up to speed quicker. And there's things that take a really long time to do. And like, the only way you're going to be able to do that job is if you spend a really long time doing it. And there are some things that are harder and something that are more agent like that have more agency. And our economy is, you know, decent at figuring that out and compensating people based on the, on the investment that they had to make and the skills they had to have to get there. But I don't think it's perfect. And I think everybody thinks and should think that the work that they do is a skill that they learn and even work at like some of the hardest jobs. Some of the hardest days I ever had at work was like working at the grill, making breakfast and burgers for people when it was super chaotic and stressful and there was no air conditioning because it had broken. And like I had to run across the street to, you know, buy extra potatoes because we hadn't prepared it right. Like, that was challenging and rewarding work. And I don't think just because I was getting paid minimum wage at the time means it wasn't valuable, but it is.

[Interviewer (Harry Stebbings)]

It is definitively less valuable.

[Nick Frost]

It's definitively paid less.

[Interviewer (Harry Stebbings)]

Adam Smith's invisible hand would suggest that it is just definitively less valuable that you went and got like more potatoes, which meant more chips for someone that didn't probably need more chips in a grill. That was a single consumption model.

[Nick Frost]

Yeah, I mean, I'm really sorry. No, no, no, I understand.

[Interviewer (Harry Stebbings)]

Your perspective is the work that your team do, which has impact on thousands of employees in some of the biggest companies in the world.

[Nick Frost]

Look, I'm glad I do the work I do now. Exactly.

[Interviewer (Harry Stebbings)]

Which has downstream impact on millions of Fujitsu users or. Yeah, it is legitimately, definitively less impactful and less valuable work.

[Nick Frost]

Yeah. Again, like, as with a few times over this, like, there's some extreme that says the invisible hand is absolutely accurate and like, whatever you're paid is exactly as much value you're creating and exactly as much value as you're worth. And then there's another side of it that's like, whatever, everything is the same. Who knows? Who has any idea? It's all the same. Right. There is some middle ground.

[Interviewer (Harry Stebbings)]

But. So going back, why does it matter that you have a generational company after we detoured it?

[Nick Frost]

One thing that comes to mind is like, look upon my works in despair. Ozymandias. And the idea of people obsessing over their legacy and building some statue to their grandeur. And one day it will also fall. One day all that will be left are two legs in the desert. That's true. And that's true regardless of what you build. But when I think about building what excites me about building Cohere. And when I say a generational company, I mean timescale generations. I don't mean my generations. I mean the idea of building something that is there for a long time, it's rewarding. And I think it's inherently human. You know, like, I think we all like to think about what are we building, how long is it going to be there? And whether that's like a work of art or an actual building or a company or a philosophy or an idea, the idea of building something or participating in the construction of something that is bigger than you is rewarding totally and is like, fundamentally human. Even though at some point, yes, it will be two feet in the desert. Both those things are true. You know, it's it's rewarding and exciting and ultimately, as with all things.

[Interviewer (Harry Stebbings)]

What was the biggest disagreement that you and Aidan have had?

[Nick Frost]

That's also a good question. The biggest disagreement. We had some disagreements about like API design. There was a brief moment like before RLHF where we were talking about like, oh, we should make an endpoint for summarization, an endpoint for entity extraction or something. And I think we disagreed about that. So we disagreed on like some low level stuff. But beyond that, you know, like I've had the privilege of getting to work with, with both Aidan and Ivan, the other co founders. Like I, you know, I have a huge amount of respect for. And we definitely disagree and argue about like the, the little things about how to run the business. Should we do this or should we make that policy? But there hasn't been any.

[Interviewer (Harry Stebbings)]

Like I was chatting to Aravind from Perplexity a month or so ago. Yeah, I actually chatting to him. It sounds so like behind the scenes, on stage, in front of 4,000 people.

[Nick Frost]

Or whatever it was. It wasn't a problem.

[Interviewer (Harry Stebbings)]

Private conversation. And I said to him, it feels to me like you, Sam Dario, all leaders of foundational model companies, you basically just kind of like presidents who sit on top of the machine kind of shouting views. Because we're in a shouting views world and then everyone in the machine does the work. And he was like, that's exactly what we're all doing. Yeah. Wow me, Sam Dario, we just have to be like in front of every camera, doing every interview, basically espousing the views of the organization constantly because it is so important to be front and center and relevant today. Do you feel that Cohere is telling your story enough publicly?

[Nick Frost]

But they're all consumer companies. They all fundamentally make money via subscriptions.

[Interviewer (Harry Stebbings)]

From consumers and they anthropic, quasi. I mean you'd say majority is enterprise.

[Nick Frost]

I would say a lot of it's most like API calls from coders. Right. Like that's a huge amount of their. So like, you know, maybe it's like I think whatever 50% cursor or something. And now. Yeah, competing with cursor. But a lot of it still like comes down to an individual decision of a consumer.

[Interviewer (Harry Stebbings)]

Yeah.

[Nick Frost]

So there I think I understand their motivation. Like, yeah, if you're, if you're selling to consumers, you want to be telling a story. Consumers are really interested in that story right now. Yeah, that kind of makes sense for them. That's not what we're doing. You can't, you can't spend 200amonth on cohere. As a person, we don't have that offering, so it's not as important for us. Like, could we be doing a better job telling what we're doing? And when I'm asked to come on here, like, yeah, I'm excited to come. I'm excited to talk to you. I like talking about cohere. I think it's important. Do I think it's the most important thing? Like, no, building a product is the most important thing. I think solving problems for our customers is the most important thing. I think making a better model for them is the most important thing. Telling our story.

[Interviewer (Harry Stebbings)]

I think that's more important than discussing Adam Smith's Invisible Hand with me.

[Nick Frost]

I do, though. I did enjoy. But yeah, I do.

[Interviewer (Harry Stebbings)]

That's hilarious. One area that we haven't covered, which is interesting is the area of sovereignty. We're sitting in London now and Mistral's in Paris. And we all say that for Mistral, it's like the Europe play and that's why it's funded and it's continued to be funded. Do you think that we will see sovereign models and usage because of geography?

[Nick Frost]

I think this technology is a lot like infrastructure, right? Like, I think building, like having a language model that speaks the language of your country is like building infrastructure for the people of your country. So I think that's broadly a good idea. The past, like 20 years longer of technological history has been very defined by Silicon Valley. And I think a lot of people are not very happy about that. A lot of people are rightfully upset with some of the developments. Like, I used to be a real technological optimist. Like, I used to love the way technology was built and be like, oh, it's so exciting, or something. And I wouldn't describe myself as a technological optimist over the past 10. 10 years.

[Interviewer (Harry Stebbings)]

Wow. Why? What happened to change that?

[Nick Frost]

Oh, well, wait, sorry, let me answer that first. Let me get back to the sovereignty thing before I go on this tangent. So, yeah, I think there's a lot of people who are interested in building that infrastructure within their country and having the technology for their economies. Just using a model that is built by China or built within America might not set your country and your economy up as well as having a model that understands the context built in that language, in that dialect, in like, you know, has the. Has the cultural fluency needed to empower the people of the country. So I think that's like a good idea. What that ends up looking like. I'm not exactly sure.

[Interviewer (Harry Stebbings)]

Geopolitics obviously influences a lot. Do you think geopolitics has influenced customer decisions around sovereignty of models? In the discussions that you see, I.

[Nick Frost]

Think US being Canadian is an asset that's helpful for people.

[Interviewer (Harry Stebbings)]

Do Canadian companies want to buy you more?

[Nick Frost]

Companies around the world are interested in talking to us and in part that's because we're Canadian. You know, over the past few years, you know, America has shown that they're willing to like turn off access to tech based on political reasons. You know, we've seen connections between American tech and the American government is like less clear as time goes on.

[Interviewer (Harry Stebbings)]

What does that mean? It means that Trump influences US tech companies.

[Nick Frost]

Seems to be. Yeah, yeah, right. I mean, I mean even. It was like last week or something, they announced they're taking a 10% stake in Intel. Right. Like, that's an interesting development. I'm not an economist and I don't know if that's good for the country or not, but it is an interesting development. So I think there's a lot of companies in Canada and around the world that are interested in working with non American tech companies and I would say that's been an asset.

[Interviewer (Harry Stebbings)]

Do you think governments should fund sovereign models? Is it a European imperative for us to have Mistral as an asset for Europe?

[Nick Frost]

I think it's a good idea for countries to have infrastructure within their countries. Like, I think it's a good idea for people to have power plants in the country. You know, I like that Canada has several nuclear power plants and has several water power plants. Like, you know that. That's great. Language models are not that dissimilar from infrastructure.

[Interviewer (Harry Stebbings)]

Do you think our primary input device will still be a phone in five years time?

[Nick Frost]

I do think language, like, I know language is going to be a more important part of it. Like, I think fundamentally the way we should be interacting with computers is using language for the majority of it, not all of it. There are times when like language is actually not the best way of interacting with a computer. It's much better to have a graphic user interface where you're like doing something. I know, like last year there was a, There was like the rabbit R1, there was those, like the humane pin and like none of they didn't really get it right. But I think there's something cool there about like, hey, how do we use a language model to work with a computer better? I haven't seen it done right yet and I don't know if it will and I don't know if that's because going. Going back to the technological optimism thing, like, you know, I was really excited when Google Glass came out. I thought that was really cool.

[Interviewer (Harry Stebbings)]

Yeah, so was I. Yeah. And then I had it as a profile picture.

[Nick Frost]

Yeah. Yeah. And then I got on a bus one time, and somebody was wearing a Google Glass, and they were delicate. They were like. And suddenly everybody saw it immediately. You know, I was really excited about VR for a while, and then I realized I actually don't want to strap a computer to my face. I'm not interested in being disengaged from the world more. I want to be engaged in the world more than I am. I don't. I don't want more things removing me. Me.

[Interviewer (Harry Stebbings)]

Do you just worry about. This is so messed. Like, just the state of the world in terms of depression, in terms of loneliness, you know, the biggest pandemic, epidemic, whatever we want to. I never know the difference between pandemic and epidemic.

[Nick Frost]

This is such a podcast. I haven't done many podcasts. This is the most podcast podcast I've ever.

[Interviewer (Harry Stebbings)]

Again. I can just do it. It's too interesting where I'm like, yeah, no, no.

[Nick Frost]

These are interesting questions. I'm happy to talk about.

[Interviewer (Harry Stebbings)]

Just so nervous about the state of loneliness, insecurity, eating disorders. Focus on materiality for young people. The number one job that any young person wants to be is an influencer.

[Nick Frost]

Yeah, there are. There are things that you're talking about that I do worry about. I do worry about. Yeah. The dissolution of community. And I think, like, what I'm talking about earlier was saying I want to be engaged more in the world. I want the technology that I use to connect me to the world better. I don't want it to disconnect me from the world. I think a lot of people feel that. I think a lot of people are looking for ways of connecting to technology more. Right. Like, I play music. A lot of the reason I play music is because it's immediate. It connects you to the people who you're playing with and the people who are listening. And a lot of the people who come listen to the music are there to connect in the moment. So I think a lot of people are feeling that, and I think a lot of people are feeling that because of the. Because they're experiencing what you're describing statistically in their personal lives. I also know that that worry of, like, oh, no, the world these days is, you know, so bad, and things are going in the wrong direction, and the kids these days are so weird and, like, oh, no, back. You know, things used to be better back then is historically ubiquitous. And everybody has always thought that going back to, like Greek philosophers bemoaning the prevalence of writing because it was going to, you know, make people not use their memories anymore and saying, oh, no, the kids these days don't understand honor. And like, going back to people bemoaning a spread of newspapers because they were all sitting on the bus reading newspapers as opposed to sitting on the bus, like, talking to each other. Like, I think two mutually exclusive things. One, yes, I'm worried about all the stuff you're talking about. And I think technology and people who make technology need to think very hard about if their technology is helping with that or hurting that. Two, this. Everybody's always thought that the time that they're alive is the time when it's the worst. And you have to hold both of those two conflicting views in your mind.

[Interviewer (Harry Stebbings)]

We'll do a quick fire, but I'll send you after this. A brilliant song. And it's not really a song. It's like a commencement speech by Baz Luhrmann. And it's called Wear Sunscreen. And it basically says exactly this, which is every generation always looks back and goes, oh, prices are so high today, and kid is so rude today. And it was better in my time. It's this kind of continuous pattern of life in terms of looking back and thinking it was better than the one we have today.

[Nick Frost]

Yeah, that. I mean, look, talking about intrinsically human things like that also seems to be intrinsically human. You know, wanting to be part of something bigger, wanting to build something that it lasts you human thinking things were better when you were young.

[Interviewer (Harry Stebbings)]

We're going to do a quick fight. If you were Sam Altman today, what would you be doing that he's not doing?

[Nick Frost]

I don't think Sam Altman has done a service to the world by talking about how close AGI is. I think he has made several predictions now that are wrong and that were obviously wrong at the time he made them.

[Interviewer (Harry Stebbings)]

Which one is most prescient?

[Nick Frost]

Oh, that AI is going to kill the whole world in two years. He's made allusions to things. Like he did a world tour where he spoke to every major leader the world over to tell them, hey, this technology poses an existential threat. And I think that was academically disingenuous and I think did a disservice to the technology he loves.

[Interviewer (Harry Stebbings)]

Do you not see a correlation between the words that one has with regards to the future of AGI and AI? And their requirements for funding, I don't.

[Nick Frost]

Know what that means.

[Interviewer (Harry Stebbings)]

Do you see what I mean? Which is for a long time do not need funding. And they are much more balanced, neutral. And then other people who do need funding have to be much more provocative and out there because I need your fucking dollars.

[Nick Frost]

Yeah, yeah. I don't know if that was the strategy. The correlation you're pointing at exists. I would say that, you know, we're a venture capital funded company and we need funding and we don't say that.

[Interviewer (Harry Stebbings)]

What worries me though actually is even the rhetoric from your Damas and your Zuck has changed. Even their aggression towards the changes that are coming has flipped, which does make me worry.

[Nick Frost]

For what reason?

[Interviewer (Harry Stebbings)]

For the reason that actually we are far closer than we think to very material shifts in labor patterns, workforce behaviors, when even Zuck who does not need the money from anyone, or Damas who doesn't need it from anyone, is going, oh shit, the changes are real.

[Nick Frost]

Yeah, well, there are some real changes, right? Like I'm not, I don't want to dent, you know, this technology fundamentally transformative. The same way the personal computer fundamentally transformative. The industrial revolution, steam engines, the printing press, those are all big technologies. There's tons of legitimate things to talk about and I'm glad people are talking about them. There's also a whole lot of lot legitimate things to talk about as people are spending their time on.

[Interviewer (Harry Stebbings)]

What is your founder ritual after closing each round?

[Nick Frost]

Who told you about that?

[Interviewer (Harry Stebbings)]

Jordan.

[Nick Frost]

That's funny. We go to McDonald's.

[Interviewer (Harry Stebbings)]

You go to McDonald's?

[Nick Frost]

We go to McDonald's. Yeah.

[Interviewer (Harry Stebbings)]

And have the same meal.

[Nick Frost]

Yeah. I normally get two junior chickens. I don't remember why that started.

[Interviewer (Harry Stebbings)]

Wow. What's the worst thing that could happen with regulation towards AI?

[Nick Frost]

I think the worst thing that could happen is that out of an erroneous understanding of the technology and thinking that what we're building is digital gods, which is large languages models are not. But if you think that that's what they're building, then you could think, okay, cool, we need to come up with benchmarks around existential threats. There are times when looking at those, at benchmarks like fixation on particular benchmarks which can be gamed and can be trained either to do way better on or way worse on are not helpful for establishing how the technology can be used and misused. So I think if you were like the worst thing a regulation could do is say, hey, we're going to pick this random benchmark. We think that represents AGI and we're going to shut down any development on it. I think that would be a misplay.

[Interviewer (Harry Stebbings)]

Do you think China will produce leading models in the next two years that continue to beat us Models?

[Nick Frost]

No, I'm not sure. They haven't yet. They've made good models, definitely good models, but I don't think they've made models that are like beating the other models out there.

[Interviewer (Harry Stebbings)]

You're not worried by China in their model capabilities? When I look at Cadence of China in a week about a month ago, there were seven new model providers who released seven models. They were pretty good.

[Nick Frost]

Yeah. I don't think I'm worried about it. I think they're going to keep building models and those models will be useful and they'll be particularly good and the things that they've trained them on. But that doesn't cause me like a bunch of.

[Interviewer (Harry Stebbings)]

What's your boldest prediction for LLMs in 2026?

[Nick Frost]

In 2026, you'll be able to open up a computer, log into north or whatever application you're using and say, file my expenses. And then the model will figure out what expense policy it is and where the photos are. Do all of that for you. That's my boldest prediction. And I know that's not very bold. I know in some ways that's like, oh, that sounds like not too far off. And yet getting it to actually work and be a thing you can rely on is not in every company. Most people don't have the experience I just described, and I think that becoming a ubiquitous way of using a computer is crazy.

[Interviewer (Harry Stebbings)]

If you weren't at cohere, which AI company would you bet your career on?

[Nick Frost]

Google's great DeepMind is building cool stuff. You know, that's exciting.

[Interviewer (Harry Stebbings)]

Have there been any tools that you've added to your workflow that meaningfully increase your productivity? So, like, for me, like Whisper Flow.

[Nick Frost]

Oh, Whisper. Yeah. Cursor. Cursor's a great coding app.

[Interviewer (Harry Stebbings)]

Do you mandate cursor across the whole ENG team?

[Nick Frost]

No. Not at all. No.

[Interviewer (Harry Stebbings)]

Does anyone use Windsurf or Devon?

[Nick Frost]

I think so. I'm not sure. We don't mandate one. I know lots of people rely on models.

[Interviewer (Harry Stebbings)]

Are you price sensitive towards cursor costs increasing?

[Nick Frost]

No, but I live a life of privilege, so, like, no, I'm not as.

[Interviewer (Harry Stebbings)]

A person, but when Costco's up 10x and you have 100 engineers.

[Nick Frost]

Oh, as a business? Yes, as a business, obviously.

[Interviewer (Harry Stebbings)]

How does that shake out? Do we see this kind of like, for us?

[Nick Frost]

Yeah. I mean, we make our Own models. So if we wanted to use our own model and plug that into an extension of VS code, that would be something we could do.

[Interviewer (Harry Stebbings)]

If that was, would the quality of output be similar right now?

[Nick Frost]

No, no, no, no. Cursor's built a good product like they built. You know, they've done really good UX stuff and it's, it's cool. But if it was to go up ten hundred fold, of course then we'd start thinking that.

[Interviewer (Harry Stebbings)]

Will we see a trillion dollar AI company outside of the US in the next decade?

[Nick Frost]

Yeah, maybe north of the border.

[Interviewer (Harry Stebbings)]

Okay. There's one other one that I love which is like, what trait do you love? And has contributed to a lot of your success, but you're also quite wary of.

[Nick Frost]

Of me. Oh, oh, that's much easier to answer. I'm quite curious and contrarian and that is both an asset and a hindrance. There are times when it's very helpful to be like, oh yeah, I'm like super interested in something and I learn about it. And then I'm like, everybody thinks this and they're totally wrong. That's super helpful. That's why when Aidan was like, hey, do you want to found a company on language models back in 2019? I was like, yeah, absolutely. You know, that was not a view that was widespread. So I'm yeah, curious and contrary. But there's other times when like the whole world has been definitely right and I've been wrong and I've been like, oh yeah, I was super excited about this.

[Interviewer (Harry Stebbings)]

When were you most wrong?

[Nick Frost]

Wrong? I'm most wrong. When I was very young, I was very much like a technological optimist as mentioned. But I thought like, cool, all metrics of human improvement are going to continue to go up. You know, like life expectancy will go up, income inequality will go down, happiness will go up like as. Yeah, we're just the path towards human endeavors and success is monotonic. And that's not true. You know, there are times like that that was something I was really wrong about. Yeah. Another thing I was wrong about was the efficiency, the data efficiency of reinforcement learning from human feedback. That was just to give a real technical answer. This is back in like 2020. And I remember being like, ah, no, you can't. You can't make a small data set of feedback from people and make a model better. That was a technological misstep. Nick.

[Interviewer (Harry Stebbings)]

This has been a interview of many twists and turns. It's the joys of doing what I do though, actually, which is like the natural conversation that's inspired. So thank you so much for agreeing to partake in such a wide ranging conversation.

[Nick Frost]

Thanks for. Thanks for having me.

[Harry Stebbings]

I want to say a huge thank you to Nick for joining me in the studio and if you want to watch the episode in full, you can find it in video on YouTube by searching for 20VC. That's 20 VC on YouTube. But before we leave you today, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of of all the information, the data and the projects that we're working on across dozens of platforms, products and tools. That's why we use Coda, the All In One collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spreadsheets, Coda facilitates deeper teamwork and quicker creativity. And their turnkey AI solution, the intelligence of Coder Brain is a game changer. Powered by Grammarly, Coda is entering a new phase of innovation and expansion, aiming to redefine productivity for the AI era. Whether you're a startup looking to organize the chaos while staying nimble, or an enterprise organization looking for better alignment, Coda matches your working style. Its seamless workspace connects to hundreds of your favorite tools including Salesforce, Jira, Asana and Figma, helping your teams transform their rituals and do more faster. Head over to Coda iO20VC right now and get six months off the team plan for startups for free. That's Coda C o D A.IO 20 VC and get six months off the team plan for free. Coda IO 20 VC and while Coda keeps our team aligned, let's talk about Brex, the ultimate financial stack for startups. So when Brex was founded, it wasn't just about creating another financial product. It was about solving the the really gritty challenges that founders face daily. Let's be honest, building something from the ground up is hard enough without dealing with clunky, outdated banks that pile on fees and leave your cash idle. Brex is different. It's the financial stack that scales with you no matter where you are in your journey. From corporate cards to maximising your Runway to earning yield on your cash, Brex was designed with founders in mind to make every dollar go further so you can focus on building. And here's what really stands out to me. Brex combines the best of checking treasury and FDIC insurance in one powerhouse account. You can send and receive money globally at lightning speed, earn Yield from day one and still access your funds whenever you need. Plus, with 20x the standard protection through program banks, your cash is not just working harder, it's working safer too. It's no surprise that 1 in 3 venture backed startups in the US with companies like Anthropic, Coinbase and Robinhood. My God, these companies are incredible. Trust Brex to help them grow. If you want to join the smartest startups on the planet, head over to brex.com startups and see what they can do for you. And speaking of incredible companies, don't forget what really keeps those customers coming back. Trust is the ultimate currency in business and today customers expect it faster than ever. And that's why over 10,000 global companies trust Vanta. Vanta automates up to 90% of the work for in demand compliance standards like SoC2, ISO 27001 and more, using smart AI to centralize workflows, manage risk and get you audit ready in weeks, not months so you can stop chasing paperwork and start closing deals. And a new IDC report found that Vanta customers achieved $535,000 per year in benefits. That's insane. And the platform pays for itself in three months. I had no idea about these Whether you're growing fast or just getting started, Vanta connects you with trusted auditors and experts support to help you build trust with customers. Get $1,000 off your first year at vanta.com 20vc that's vanta.com 20vc as always, I so appreciate all your support and stay tuned for an incredible episode on Thursday with Jason Lamkin, Rory o' Driscoll and a special guest in the form of Canva co founder Cliff Obrecht.