20VC: Chips, Models or Applications; Where is the Value in AI | Is Compute the Answer to All Model Performance Questions | Why Open AI Shelved AGI & Is There Any Value in Models with OpenAI Price Dumping with Aidan, Gomez, Co-Founder @ Cohere

Aidan Gomez is the Co-founder & CEO at Cohere, the leading AI platform for enterprise, having raised over $1BN from some of the best with their last round pricing the company at a whopping $5.5BN. Prior to Cohere, Aidan co-authored the paper “Attention is All You Need,” which introduced the groundbreaking Transformer architecture. He also collaborated with a number of AI luminaries, including Geoffrey Hinton and Jeff Dean, during his time at Google Brain, where the team focused their efforts on large-scale machine learning. In Today's Episode with Aidan Gomez We Discuss: 1. Compute vs Data: What is the Bottleneck: Does Aidan believe that more compute will result in an equal increase in performance? How much longer do we have before it becomes a case of diminishing returns? What does Aidan mean when he says "he has changed his mind massively on the role of data"? What did he believe? How has it changed? 2. The Value of the Model: Given the demand for chips, the consumer need for applications, how does Aidan think about the inherent value of models today? Will any value accrue at the model layer? How does Aidan analyze the price dumping that OpenAI are doing? Is it a race to the bottom on price? Why does Aidan believe that "there is no value in last year's model"? Given all of this, is it possible to be an independent model provider without being owned by an incumbent who has a cloud business that acts as a cash cow for the model business? 3. Enterprise AI: It is Changing So Fast: What are the biggest concerns for the world's largest enterprises on adopting AI? Are we still in the experimental budget phase for enterprises? What is causing them to move from experimental budget to core budget today? Are we going to see a mass transition back from Cloud to On Prem with the largest enterprises not willing to let independent companies train with their data in the cloud? What does AI not do today that will be a gamechanger for the enterprise in 3-5 years? 4. The Wider World: Remote Work, Downfall of Europe and Relationships: Given humans spending more and more time talking to models, how does Aidan reflect on the idea of his children spending more time with models than people? Does he want that world? Why does Aidan believe that Europe is challenged immensely? How does the UK differ to Europe? Why does Aidan believe that remote work is just not nearly as productive as in person?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  The reality of the matter is, there's no market for last year's model. It's definitely true that if you throw more compute at the model, if you make the model bigger, it'll get better. For folks who have a lot of money, that's a really compelling strategy. I think we'll continuously exist in a world of multiple models, some focused and verticalized, others completely horizontal. There's going to be a consolidation in the space, for sure. It's really dangerous when you make yourself a subsidiary of your cloud provider.

• 0:26Harry Stebbings?

  I am so excited for this 20 VC. I've had Jan La Coon, Sam Altman, founders of Adept, Mistral, Character AI, but I was so keen to have Aidan Gomez, founder of Cohere on the show. And today, we make it happen. For those that do not know, Aidan, as I said, co-founder and CEO of Cohere, the leading AI platform for enterprise, having raised over a billion dollars with their last round pricing the company at a whopping $5.5 billion. And prior to Cohere, Aidan co-authored the paper, "Attention is All You Need", which introduced the groundbreaking transformer architecture. He also collaborated with a number of AI luminaries, including Jeff Hinton and Jeff Dean, during his time at Google Brain. This is an incredible episode, and it was fantastic to have Aidan in the studio in London to make it happen.

• 1:11Harry Stebbings?

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• 1:56Harry Stebbings?

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• 2:40Harry Stebbings?

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• 3:23Harry Stebbings?

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• 3:42Harry Stebbings?

  You have now arrived at your destination. Aidan, I am so excited for this. So I was going through the prep first before I was writing the schedule. And I was thinking, where do we start? And then I saw one of the notes. And Aidan, it says that you grew up or were brought up in rural Ontario in a house your grandfather or father built by hand. What was that like as a starting point? And can you take me there?

• 4:08Speaker 00

  Yeah, I grew up in the middle of nowhere in Ontario. It was a big 100 acre lot and it's a maple forest. And so it was super cool to grow up in like the most Canadian environment ever. But it was very distant from technology, for sure.

• 4:26Harry Stebbings?

  But you loved gaming, didn't you?

• 4:28Speaker 00

  I did love gaming. So I loved technology from, from scratch. It's just, it was really hard to access it. Like, we couldn't get internet. We could do dial up, but I had dial up for years after people had gotten high speed internet. And so all my friends, you know, they were online gaming, doing all this sort of stuff. And I was just so jealous, or not jealous, but just like missing out on this wave of technology, the internet that was coming about and becoming popular. So it made me obsessed with tech. I would like sit at home with our computer with shitty dial up internet. And I would just try to make it faster. I'd try to make the most out of what I did have. And eventually that led to wanting to learn how to code and understand how the web works. And, you know, can I make this stuff faster? Can I load the internet faster?

• 5:12Speaker 00

  Cause I was watching pixels go line by line. And that's really what pushed me into CS, forced to learn how this tech works so that I could get more out of it.

• 5:20Harry Stebbings?

  This understanding that I have now from meeting so many incredible founders. And it's this incredibly high correlation between those that gamed in their early years and those that achieved success. Why do you think gaming is such a contributor to successful founders?

• 5:33Speaker 00

  I think video games teach something to you. You're much more willing to grind, to just do repetitive, difficult, painful things towards some broader goal. So that sort of resilience, I think is important. And then also the fact that you can respawn, like you get to try again, you get a second attempt. That optimism or that, that framing is really important. I think in a lot of cultures, you get one shot. You have a reputation and if you fuck it, it's done, it's over for you. Maybe what gaming can give people is a sense of you can fuck up and you can try again and you can get better. And the second time you fuck up less than the first time. And the third time you fuck up less than the second time. And so that notion of progress through failure, I think is probably something very significant for founders.

• 6:17Harry Stebbings?

  I also always believe in the power of like game design and like progressive overload. The way that games are designed to be easier at first, you feel great, you pick up confidence. You would never start a game with a really hard first level where people fail. And it's like, this is impossible. I'm not going to do it.

• 6:32Speaker 00

  Yeah, I mean, there's analogies. So in in machine learning, that's called curriculum learning, like that you want to start. Okay, let's first teach the model to do something very simple, to make it a little bit more complex and build on that knowledge. What's funny is that curriculum learning has actually failed in machine learning. We don't really do curriculum learning. It's just throw the hardest material and the easiest material all at the same time and let the model figure it out. But yeah, for humans, it's it's so effective. It's such an important piece of how we learn. It's interesting to see that hasn't taken off.

• 7:00Harry Stebbings?

  You said about kind of just throwing it at the model. I just wanted to dive in at the deep end bluntly, because I think it's a question that everyone's asking, which is like, everyone just says, just throw more compute. And that is the single biggest rate limit that we have today. We just need more compute and performance will increase. Do you think that is true? There is a lot more room to run there? Or it is other elements that are now holding back performance?

• 7:24Speaker 00

  It's definitely true that if you throw more compute at the model, if you make the model bigger, it'll get better. It's kind of like, it's the most trustworthy way to improve models. It's also the dumbest, right? Like, if all else fails, just make it bigger. And so for folks who have a lot of money, that's a really compelling strategy. Super low risk. You know it's going to get better. Just scale the model up, pay more money, pay pay for more compute and go. I believe in it. I just think it's extremely inefficient. There are much better ways. If you look at the past, let's say, like, year and a half. So between, I guess by now, it would be like between ChatGPT coming out or GPT four coming out and now, GPT four, if it's true what they say, and it's 1.7 trillion parameters, this big MOE, we have models that are better than that model that

• 8:09Speaker 00

  are like 13 billion parameters. Right? And so the scale of change, like how quickly that became cheaper is absurd, kind of surreal. And so, yes, you can achieve that quality of model just by scaling, but you probably shouldn't.

• 8:24Harry Stebbings?

  Do we continue to see that same scaling advantages? Or does it actually plateau at some point? As you said, there, we always hear about Moore's law. At some point, it just becomes a better calculator for the iPhone.

• 8:35Speaker 00

  It certainly requires exponential input. You know, you need to continuously be doubling your compute in order To sustain linear gains in in intelligence. But I think that probably goes on for a very, very, very long time. It'll just keep getting smarter. But you run into like economic constraints, right? Not a lot of people bought the original GPT four, certainly not a lot of enterprises. Because it was huge, it was massive, super inefficient to serve, so costly, not smart enough to justify that cost. There's a lot of pressure on making smaller, more efficient models, smarter via data and algorithms, methods, rather than just scaling up due to market forces, just pressure on price.

• 9:18Harry Stebbings?

  Well, we live in this world of unbundled, verticalized models, which are much more efficient and smaller, designed for specific use cases. Or there'd be much larger three to five models, which kind of rule it all.

• 9:30Speaker 00

  There will be both, there'll be both. The one pattern I think we've seen emerge over the past couple of years is that people love prototyping with a generally smart model. They don't want to prototype with a specific model. They don't want to spend the time fine tuning a model to make it specifically good at the thing that they care about. What they want to do is just grab an expensive, big model, prototype with that, prove that it can be done, and then distill that into an efficient focus model at the specific thing they care about. That pattern has really emerged. I think we'll continuously exist in a world of multiple models, some focused and verticalized, others completely horizontal.

• 10:09Harry Stebbings?

  You spoke about kind of the cost and needing to double compute to keep that same kind of linear level of intelligence. Cost is exorbitant. Maybe I'm wrong here and I'm too young to remember past technology cycles, but almost unlike anything we've seen before in technology. We, I think it was three billion a year that OpenAI is spending. How can you afford to maintain your position in this race unless you are Microsoft, Amazon, Google or Facebook?

• 10:31Speaker 00

  I think if you're just doing the scaling project, you have to be one of those. Or you have to be an effective subsidiary of one of those companies. But there's a lot more to be done. Like if you if you're not completely adherent to scale as the only path forward. If you believe that there are data innovations, there are model and method innovations.

• 10:51Harry Stebbings?

  Can we just? More data innovations and modern model and method innovations.

• 10:55Speaker 00

  Yeah, so pretty much all of the major gains that we've seen in the open source space have come from data improvements. Models getting much better by taking higher quality data from the internet, better scraping algorithms, parsing those webpages, pulling out the right parts, upweighting specific parts of the internet, because there's lots of repetition and junk, right? And so pulling out the most valuable, knowledge-rich parts of the internet and emphasizing them to the model. Synthetic data and the ability to create new data that is super scalable. So you can get many, many billions of words or hundreds of millions of pages of this stuff. But it's no humans involved, just written by models.

• 11:39Speaker 00

  Those innovations, the ability to increase the quality of data, have led to most of the gains that we're seeing right now.

• 11:46Harry Stebbings?

  Okay, so that's data innovation, method and model innovation.

• 11:50Speaker 00

  Yeah, so this is stuff like new URL algorithms. There's lots of rumors about Q-Star and what that might be. Ideas around search, searching for the solution. The status quo with models is I ask you a question and the model is expected to respond immediately with the right answer. That's an incredibly high burden to place on the model, right? Like you couldn't do that to a human. You couldn't ask a human a hard question and expect them to just regurgitate the answer immediately. They need to work through it.

• 12:18Harry Stebbings?

  Have you ever been in a board meeting?

• 12:19Speaker 00

  Yeah, sometimes we do, sometimes we do. Yeah, there's this very obvious next step for models, which is you need to let them think and work through problems. You need to let them fail. They need to try something, fail, understood why they failed, roll that back and make another attempt. And so at present, there's no notion of problem solving in models.

• 12:41Harry Stebbings?

  And when we say problem solving, that is the same as reasoning, correct?

• 12:45Speaker 00

  Yeah, yeah.

• 12:46Harry Stebbings?

  Why is that so hard? And why do we not have any notion of that today?

• 12:50Speaker 00

  I think it's not that reasoning is hard. It's that there's not a lot of training data that demonstrates reasoning out on the internet. The internet is a lot of the output of a reasoning process. Like, you don't show your work when you're writing something on the web. You sort of present your conclusion, present your idea, which is the output of loads of thinking and experience and discussion. So we just lack the training data. It's just not freely available. You have to build it yourself. And so that's what companies like Cohere and OpenAI and Anthropic, et cetera. That's what we're doing now is collecting data that demonstrates human reasoning.

• 13:25Harry Stebbings?

  How do you think about competing against like OpenAI's incredible UGC play?

• 13:29Speaker 00

  Yeah, no, that's super difficult. And especially with enterprises, they never let you train on their data. And so we can't train on any of our customers' data, super private. Their perspective is their data is their IP. There's too many secrets in their IP. And so they're just not willing to do it. And I'm super empathetic to that. And so for us, our focus is synthetic data. We push a lot on that, as well as having like a human annotation force and scale as a partner for that. We have our own folks in-house, but that's the burden that's placed on us because we're not a consumer company. We have to generate this data ourselves. The benefit is we're more focused, so we have less surface area to cover. So it's not the entire world showing up and asking us to do potentially anything.

• 14:10Speaker 00

  It's like enterprises with very clear patterns for the type of stuff they want to do. It's like they want to automate certain finance functions or they want to automate HR functions. And so the scope is reduced dramatically, which lets us really focus in on on those pieces.

• 14:25Harry Stebbings?

  What will the synthetic data market look like in 10 years? And will it be won by two to three providers?

• 14:30Speaker 00

  I mean, I've heard that the current LLM API market is dominated by synthetic data. That's mostly what people are doing. They're creating data from these big expensive models to fine tune smaller models that are more efficient. So they're ostensibly like distilling the bigger models. I don't know how sustainable that is as a market. But I definitely think there's always going to be a new task or new problem or new demand for for data. And whether that comes from models or whether it comes from humans. We're gonna have to to meet the demand.

• 15:02Harry Stebbings?

  One thing I'm concerned about, Bunny, or I look at with hesitation, is you see OpenAI price dumping. You see Meta releasing for free and Mark pronouncing the value of open source and an open ecosystem. Are we seeing this real diminishing value of these models? And is it a race to the bottom and a race to zero?

• 15:21Speaker 00

  For the next little while, it's gonna be a really tricky game. It won't be a small market.

• 15:26Harry Stebbings?

  Can I be really stupid? Who's only selling models and who's selling models and something else?

• 15:31Speaker 00

  I don't wanna name names, but let's say Coherent right now only sells models. We have an API and you can access our models through that API. I think that that will change soon. There are gonna be changes in the product landscape and what we offer to sort of push not away from that, but to add on to that picture and that product suite. But if you're only selling models, it's going to be difficult because it's gonna be like a zero margin business. Cause there's so much price dumping, people are giving away the model for free. It'll still be a big business. It'll still be a pretty high number because people need this tech. It's growing very, very quickly, but the margins at least now are gonna be very, very tight. And so that's why there is a lot of excitement at the application layer.

• 16:12Speaker 00

  And I think that discourse in the market is probably right to point out that value is accruing beneath, like at the chip layer, because everyone is spending insane amounts of money on chips to to build these models in the first place. And then above at the application layer, where you see stuff like ChatGPT, which is charged on a like per user basis, you know, 20 bucks a month type thing. That seems to be where at this phase, value is accruing. I think that the model layer is an attractive business in the long term. But in the short term, with the status quo, it is a very low margin commoditized business.

• 16:47Harry Stebbings?

  If we just kind of break it down, you mentioned kind of the chip layer there. How do you think about your spend today on chips and how that has changed over time as a percent of spend?

• 16:57Speaker 00

  Yeah, it's gotten way, way more. Yeah, it's a it's a huge chunk of our spend now, way too much.

• 17:03Harry Stebbings?

  And you have a direct relationship with Nvidia?

• 17:05Speaker 00

  Yeah, yeah, and loads of chip players, like we're we're close with Nvidia, AMD. Conversations with lots of startups that are building new chips. We also run on TPUs from Google.

• 17:16Harry Stebbings?

  And that's because you don't want to have a single point of failure?

• 17:18Speaker 00

  It's mostly because market demands it. Like, our customers want to be able to run on tons of different platforms. They want optionality. They don't want to get locked into one. And so we need to provide a really diverse base of platforms to run on. Similarly to how we've been very avoidant to get locked into one cloud and we want to be available on every cloud, it's because market demands it. Like, customers want choice. They don't want to get verticalized lock into one provider.

• 17:42Harry Stebbings?

  Totally get you. Do you think everyone will be kind of verticalizing their own stack in terms of building out their own chip capabilities? We've seen Apple recently talk a lot about kind of their own owning the chip layer too. Do you think that'll be a continuing trend or not?

• 17:54Speaker 00

  Uh, I think it will be. Right now, chips are just exceptionally high margin and there's very, very little choice in the market. That's changing. I think it's gonna change faster than other people think.

• 18:07Harry Stebbings?

  I think you've also seen the stockpiling of TPUs change a lot. You know, before there was this same real supply chain shortage.

• 18:13Speaker 00

  Yes.

• 18:14Harry Stebbings?

  And now it's not so much.

• 18:16Speaker 00

  No, yeah, the shortage is going down. I think the, it's becoming clear there are going to be more options available. And not just on the inference side. Like, everyone, inference is already quite heterogeneous. You actually already have loads of options on the inference side, which is like, not the training of the models, but the serving. On the training side, the picture has been, it's essentially one company that creates the chips that you can use to train big models. That's still true today, but actually it's not true today. There's two companies. You can definitely train big models on TPUs. Those are actually now a usable platform for super large-scale model training, and I think Google has proven that quite convincingly.

• 18:54Speaker 00

  And then there's Nvidia. But I think soon AMD, Trainium, these platforms are gonna really be ready for primetime.

• 19:02Harry Stebbings?

  The question that I have is when you look at the spend on the models and actually compute and you see, what worries me is actually model progression is moving so much faster than data center build out and kind of compute progression. And so it's like, when you look at a year's time, are we gonna be running the newest latest models on H100s or whatever the 18-month-old compute is? And is there a misalignment between model advancement and compute advancement?

• 19:29Speaker 00

  I mean, the supply chain thing is like really, really interesting. I think.

• 19:33Harry Stebbings?

  Do you need to build out your own data centers?

• 19:35Speaker 00

  No, we partner with folks.

• 19:36Harry Stebbings?

  Is there ever a time when that changes?

• 19:38Speaker 00

  You know what? We're an economically rational actor. If it's cheaper for us to build out our own data centers, we'll go do that. We've run the numbers and we feel confident that the price we're getting from our providers makes that not a really attractive path. The other reason we do it is if there were a chip to come out that was really attractive in its cost profile, but no provider would procure it for us.

• 20:04Harry Stebbings?

  Did you have any challenges in access to significant amounts of compute in the early days today? Has that changed?

• 20:11Speaker 00

  We've been around for like five years now, and so it was well before the whole thing started popping off. So we, we were lucky. We.

• 20:19Harry Stebbings?

  Did you expect it to pop off?

• 20:21Speaker 00

  I mean, I wouldn't have started the company if I didn't.

• 20:23Harry Stebbings?

  But like, but like over.

• 20:24Speaker 00

  Not, not in the way that it.

• 20:25Harry Stebbings?

  Cause it was a.

• 20:26Speaker 00

  Totally. It happened later and much more suddenly than I expected.

• 20:31Harry Stebbings?

  Cause you co-authored the piece in 2017 around transformers?

• 20:34Speaker 00

  Yeah, yeah.

• 20:35Harry Stebbings?

  And so you were expecting it to pop off relatively quickly, I take it?

• 20:38Speaker 00

  No, not, not at that moment. In 2017, I was kind of like, I was the intern on this transformer paper. And I thought, no, this is just research, you know, we just create new architectures, improve translation scores by three percent. And that's what it is. I didn't expect all that came of that, that architecture, the transformer and the community's love for it. And, and real like consolidation onto the transformer as a platform for building AI. That I didn't expect. With language modeling and the whole scaling project, I thought the world would catch on way faster to that piece. It started to become really obvious, but then it was two, three years before everyone woke up. And it sort of hit the world.

• 21:21Harry Stebbings?

  What was that turning point? Was it chat GPT?

• 21:23Speaker 00

  It totally was. Yeah. It was chat GPT. It was putting the technology directly in front of the user. You don't have to explain it to your mom or dad or whatever. You can like, sit down, talk to this thing, experience what it's like to talk to these models.

• 21:37Harry Stebbings?

  Do you think chat is the best interface? To consumers.

• 21:40Speaker 00

  For some stuff, I think for other stuff GUI, like you know, like a user interface, a traditional visual one is quite good. I think it really depends. Chat as an interface onto everything, I don't think makes sense. I don't want to have to type out explicitly my instructions to get stuff done. Like sometimes I just wanna click some buttons and go through a GUI and get the job done. Yeah, I don't think like GUIs are dead and that we should replace everything with a text box. But I do think it provides this really compelling interface, certainly voice does. Voice is magical. Certainly, it was magical the first time I saw a model write text back to me as compellingly as a human. That happened like in twenty seventeen, shortly after we submitted the paper.

• 22:23Speaker 00

  We started training language models on Wikipedia and we sampled from those models. And it could write Wikipedia pages as convincing as a human page. That was a very magical moment that computers kind of woke up and started speaking back to us. And then the next time was dialogue as an interface. So not just I submitted an instruction, the model returns a response, but having a conversation over chat with the model.

• 22:49Harry Stebbings?

  OpenAI investing a lot in voice. Do you think that confidence in voice as the next kind of interface with consumers is right and justified?

• 22:57Speaker 00

  Absolutely. Like anyone who has tried having a voice-based conversation with one of these models. It's like a stunning experience. You're kind of left in shock when you hear the model exhibiting emotion and inflection. And you know, you hear it breathe, inhale before it speaks, you hear its lip smacking. Like there's something so incredibly compelling about that experience. It's hard to, hard to describe until you try it for the first time. It's such a compelling interface.

• 23:27Harry Stebbings?

  I've always was brought up on the idea that actually we always overestimate things in the short term and underestimate them in the long term. To what extent do you think that's the case here? Or actually, voice is coming and coming pretty quickly. GPT five is coming and coming, whether that's in three to six months, still coming pretty quickly. To what extent are we actually underestimating the short term?

• 23:49Speaker 00

  Two things happening. One, it's getting harder to deliver gains in the models. It is getting more difficult, more arduous, more costly, because there was a time where the models were dumb enough that I could pull, say dumb enough. But the models were.

• 24:05Harry Stebbings?

  Not sophisticated.

• 24:06Speaker 00

  Yeah, sufficiently unintelligent that I could pull anyone off the street. Any human was more intelligent than the model and had something to teach it, right? I just grab someone, say, talk to this model, find errors and they will and improve it. Eventually, the models, like, it was just kind of hard to get people, the average person, to find knowledge gaps or that type of thing. And so you had to start going to domain experts. And initially, like, cheap, kind of junior ones, like, students of computer science could teach the model something. Students of biology could teach the model something. And then the model started getting really good and kind of matching that level of knowledge. You're just going into more specific and more scarce pools of talent to get them to teach the model their knowledge.

• 24:49Speaker 00

  And so it gets more high friction, more expensive to teach the model the incremental new knowledge.

• 24:54Harry Stebbings?

  When does it not become worth it? I always think about language learning, which is like, you can learn something like 95% of a language in six months. But to get to 98% proficiency, it takes five years. Kind of bastardize that stat, but it's about that. To what extent does one go, actually, for that extra incremental 0.5% increase, it's another billion dollars? That no longer is efficient.

• 25:17Speaker 00

  Yeah, I mean, fortunately, costs are falling super fast on, on everything, like, compute costs, dollars per flop, the scale of model.

• 25:24Harry Stebbings?

  Dollars per flop?

• 25:25Speaker 00

  Yeah, like, how much a flop costs.

• 25:26Harry Stebbings?

  Oh, right.

• 25:27Speaker 00

  Or flops per dollar. So a flop is a unit of compute and a model.

• 25:32Harry Stebbings?

  Sorry, dude, that's hilarious. A flop, like, for me in the UK, it's like a, yeah, like a flop, I'm saying, yeah, I completely flop that one. I was like, is this like a new thing?

• 25:41Speaker 00

  No, it's a, no, it's a super old thing. It's floating point operations. So it's literally like one clock cycle of a.

• 25:50Harry Stebbings?

  That's amazing. I'm glad I clarified.

• 25:52Speaker 00

  Yeah, yeah, yeah, no. And so if you have, like, 10 billion parameters, that basically equates to. Some number of flops. And if you ten X that, if you have a hundred billion parameters, that equates to ten X that number of flux approximately. So anyway, the the price for a flop goes down super, super quickly over time. And so that's what's unlocked much larger models today compared to twenty seventeen and even two years ago.

• 26:19Harry Stebbings?

  Given that, do you not think that actually it is not too late for a new startup to enter the model space? Because everyone's like, oh, this is far too late for a startup to enter the model space. And actually, given the decreasing cost barrier, does that not mean it's actually more accessible than ever for startups to do?

• 26:33Speaker 00

  So it becomes cheaper to build last year's model by like a factor of ten or a hundred each year. We just get better data, cheaper compute. So yeah, it definitely lowers the barrier to the previous generation of models. The reality of the matter is, nobody cares about the previous generation. Nobody wants them. There's no market for last year's model. It's like useless in comparison to this year's model. Any any sort of like technological development really makes the last generation obsolete super quickly.

• 27:03Harry Stebbings?

  I think the difference is like, it costs you ten million dollars to build V1, so I'm saying the software product. And to make V2 that update a little bit better, another one or two million dollars. But here it's like three billion to build one, and then five billion to build two. The increment is not increment. It is order of magnitude.

• 27:22Speaker 00

  I don't know if it's always the pattern that it's cheaper to build the next generation. I I think with like chips, for example, and and other very complicated pieces of technology, it does get more expensive to generate each new generation. And we still do it because it's worth it.

• 27:37Harry Stebbings?

  Going back to your statement, sorry, because I went off on a tangent there. No one gives a shit about last year's model.

• 27:43Speaker 00

  Well, you were you were asking, do the improvements sustain? And I was saying, it's getting harder to improve these models. It's getting higher friction. And the second, the second weird effect is that as these models are getting smarter, each individual's ability to distinguish between them becomes way harder. You can't tell the difference between generations because you're not enough of an expert in medicine, mathematics, physics, to to actually feel the change. Like, the model is already Kind of as good as it can get with all the basic level knowledge, which is what you and I have. And so when we interact with it, we get the same experience between generations. But in reality, those generations are changing dramatically in much more specific capabilities or raw intelligence. And yeah, you were asking, is it worth it?

• 28:27Speaker 00

  Is it worth it to keep spending so much money to push forward? And I think absolutely it is. Absolutely it is. It's worth it to someone, right?

• 28:33Harry Stebbings?

  Why?

• 28:34Speaker 00

  Even if for you and I, like as consumers, when we're using this stuff, like we don't care if it knows C star algebras and like quantum physics. It doesn't matter to us. It has no impact on our experience with this technology. But that's really helpful for a researcher in quantum physics. And so we'll make more progress there by providing tools. It's the same question around just technology in general. Like we have abundant food. We have, you know, super cheap cars now and we have phones in all of our pockets. We're kind of good, like we've got it. Should we really invest in the next generation of technology that focuses on, you know, creating a new material for a spaceship? So that it can get up into orbit more efficiently? Yeah, we should. And it might not matter to you, like you don't give a shit if the spaceship gets up into orbit cheaper.

• 29:20Speaker 00

  But it matters to someone a lot and they're willing to pay and there's a market for it. And that's how progress sustains itself.

• 29:26Harry Stebbings?

  That continuing progress, we go back to obviously costs and will continue to cost a lot of money. You said before a really interesting two words, which was effective subsidiaries. And we've seen a lot of companies be kind of bought, aquahired, whatever that is, subsumed in. I think everyone realizes now that cloud is the cash cow that keeps on giving. When you look at kind of the continuing growth rates and profitability of a zero and Google cloud and everything in between. And actually you'll just see the majority of those smaller model providing companies bought up by these large cloud providers. Do you agree with that as a probable likelihood for the next three to five years?

• 30:02Speaker 00

  Three years, yeah. Yeah, I think there will be a culling of the space. I think it's already happening. I think a lot of the model builders that

• 30:10Harry Stebbings?

  Well, Aidan's gone to Amazon. We had David on the show. He was fantastic. Love David.

• 30:14Speaker 00

  He's great.

• 30:15Harry Stebbings?

  Really, really good. Inflection, obviously gone to Microsoft.

• 30:18Speaker 00

  And I think there's more, more coming. There's going to be a consolidation in the space for sure. It's really dangerous where you, when you make yourself a subsidiary of your cloud provider.

• 30:28Harry Stebbings?

  Why?

• 30:29Speaker 00

  Well, it's, it's just not good business. So to raise money as a company, you need to go and convince some investors who they only care about ROI on that capital and they give you the money and you go create some value using it. But when you're, you're doing this raising from cloud providers thing, the math is super different.

• 30:49Harry Stebbings?

  Do you think venture investors will make money from the model investments we've seen over the last years?

• 30:53Speaker 00

  Cohere's investors will. They'll make a lot of money.

• 30:56Harry Stebbings?

  Do you look back on that and feel great for making these people who believed in you a load of money? Or you're like, fuck, that was cheap and I shouldn't have given away that much?

• 31:04Speaker 00

  No, I mean, I, I think that everyone who was there at that point is still here. They're still fighting. So our first investor was Radical Ventures, Jordan Jacobs there. He's still on our board. He's still, I, I call him like the fourth co-founder of Cohere. He's built the company alongside us and is still very active and present in, in building the company. So I, I don't regret it.

• 31:26Harry Stebbings?

  What, what was the latest price?

• 31:28Speaker 00

  The media reports that it was a little over 5.5.

• 31:32Harry Stebbings?

  Does that cause you stress? You know, when you look at revenues to valuation, I know we're not in that game, but at some point, everyone is in that game.

• 31:40Speaker 00

  Of course.

• 31:40Harry Stebbings?

  Does that, does that make you go, fucking hell, we've got a long way to go?

• 31:44Speaker 00

  It's definitely pressure. It's good pressure. Everyone gets into the revenue multiples game at some point. Some point it converges to public market multiples. We are actually in a dramatically better position than a lot of our comparables. Because our valuation is not at the crazy state that a lot of others are. That's my belief. We still have to grow into it. But I'm very confident that the market is strong. A lot of people need these models. On the margin side, it's under pressure right now because of price dumping and because of Free models being given out, but that will change over time and then cohere our product stack will also evolve.

• 32:19Harry Stebbings?

  Which one do you most respect?

• 32:21Speaker 00

  I would say OpenAI. They paved the way, like just sort of like a irrational conviction to this vision of scaling. I remember talking to Ilya about this stuff way before GPT-1, you know, like in the transformer times around that time, because he was big in the Toronto scene, he studied under Jeff. And this, this idea of scaling, it was in his head back then, years before he actually started pursuing it properly. And that conviction led to the world that we live in today. This objectively magical technology that's emerged and is now sitting available to everyone. I really admire Ilya.

• 32:58Harry Stebbings?

  Ethan Molloy from Wharton said on the show, and OpenAI really only cares about AGI and the pursuit of AGI. And so they abandoned products like code interpreter and a lot of other really useful products because they're focused on AGI. So it's not a criticism, but just like that's their focus. Do you agree with that? Or do you think they are actually kind of dual minded in terms of both going for the long term AGI and also being much more cognizant of creating short term valuable products for enterprise and for consumers more broadly?

• 33:26Speaker 00

  I mean, I think lately, or in the new OpenAI, they're like a product company. They're like hardcore building a consumer product. That is their objective and it seems like it. And it's working. People love that product. It's a household name at this point. So I think in the consumer space, they're going to be a product company. And I think that they have to become one in order to foot the bill to build what they want to build. If you look at some of the departures, I would say it seems like the AGI effort is starting to take a backseat to, to product and to the, the consumer offering.

• 33:58Harry Stebbings?

  Something that I worry about is, and I use Canva as an example of this, which is like, are we going to see companies be able to make more revenue per user from adding AI to their products? And every company is an AI product company now, whether it's Zen customer support, Notion with note taking, Canva with design. It's all AI. And you can have bluntly said on the show recently that they are having margin compression because they don't charge more per seat, but they have AI infused in all of their product. And so you can create anything with AI in their products. And obviously each query costs money. It's costing them more money and they're making the same revenue. Will we actually be able to make more revenue per user? Or will it just create a better customer experience?

• 34:39Speaker 00

  Well, I think there's two different camps right now. Some people are pricing the exact same with AI features and using it to drive expansion in their business. And then the other folks like Microsoft, like Salesforce, like Notion as well, they're charging for the AI features and getting a bigger business as a product. Both of those strategies are fine and super reasonable. For folks like Canva, who are keeping the same price, I mean, I think it's a good bet. They want to grow their user base. They want to expand their user set. Just give them the most useful product possible. At the moment, don't worry about margins because the cost of AI is falling super, super quickly. I think that's reasonable.

• 35:15Harry Stebbings?

  On enterprises, yeah, Canva is obviously making a hard push for enterprise. You sell into amazing enterprises. What's the number one blocker today for why enterprises don't adopt?

• 35:25Speaker 00

  It's mostly trust in the technology, social security. Everyone is very sketched out by the current state of things. Who's training on my data?

• 35:33Harry Stebbings?

  Sketched out means concerned?

• 35:34Speaker 00

  Yeah, yeah.

• 35:35Harry Stebbings?

  Right. Not like a flop?

• 35:38Speaker 00

  Well, they're hoping that they don't have a flop. So they're, they're really scared that someone's going to take their data, train on it, and put them in some sort of like security vulnerability, or that they'll lose IP. I think that's a very valid concern because people have been training on user data.

• 35:53Harry Stebbings?

  Is there anything you can do to reassure them other than use synthetic data?

• 35:57Speaker 00

  Yeah. So our, our deployment model is set up to do that. We focus on private deployments, like inside their VPC on-prem. Like what that means is just like, it's on their hardware completely privately. We're not asking them to send data over to us. We'll process it and give you back the response from the model. We're saying, we'll bring our models to where your data is. We can't see any of it.

• 36:18Harry Stebbings?

  Will we see the movement back to on-prem in this new world?

• 36:21Speaker 00

  When I speak to folks, it's super conflicted. In financial services, yeah, people are pulling away from cloud. They're pulling away from cloud. They're building out their own data center capacity. Everywhere else still seems to be, we need to migrate to cloud. It doesn't make sense for us to have these data centers. I think that it probably depends on the vertical that you're looking at.

• 36:39Harry Stebbings?

  What do they just get totally wrong about AI? I think the enterprise education curve is still very early. What do they just not understand about it?

• 36:48Speaker 00

  There's a lot of fear around AI being wrong. There's the hallucination in these models and everyone views that as some sort of like, the technology is doomed, you know, sometimes it hallucinates. It doesn't reflect reality. The models definitely do hallucinate. The hallucination rates have been dropping dramatically, but they'll always have some chance of making stuff up or getting something wrong. But we exist in a world with humans and humans hallucinate constantly. We get stuff wrong, we misremember things. And so we exist in a world that's robust to error. And so I think

• 37:20Harry Stebbings?

  We don't have hallucination benchmarks, so do we?

• 37:22Speaker 00

  We do, yeah.

• 37:23Harry Stebbings?

  We do?

• 37:24Speaker 00

  Yeah, yeah, like Victara has one and there are other hallucination benchmarks.

• 37:27Harry Stebbings?

  And we're seeing them decrease at the same level as model progression?

• 37:31Speaker 00

  The same level I don't know about, but definitely it's been decreasing super fast. And with RAG, it's like a step change.

• 37:37Harry Stebbings?

  And so if anyone doesn't know, RAG is?

• 37:39Speaker 00

  A Retrieval Augmented Generation. So it's the idea that you have a model.

• 37:42Harry Stebbings?

  Thank you for that description.

• 37:44Speaker 00

  The idea that you have a model which can query out to a knowledge base. And that knowledge base might be your internal documents or a search engine. It might write a query to a search engine, pull back the results and then use that as part of its answer and cite its sources. So it's saying, I'm making this claim because I read it over here. So now you can audit whether it's correct. And it also, as a byproduct of the setup, really stops lying as much. It doesn't have to make up as much because it has reference material it can draw from.

• 38:11Harry Stebbings?

  And that's a game changer.

• 38:12Speaker 00

  For hallucination, definitely. And also just for like customizing the models because they've seen the public web. So they know a lot about public information. But for private stuff, I want my model to be able to answer questions about my email inbox, which is something only I have access to. And so the ability for the model to query my email inbox, pull back that information, it just makes it more knowledgeable about the stuff that I care about.

• 38:33Harry Stebbings?

  Are we still in experimental budgets for enterprise? Everyone's like, no, we're just playing with budgets now. Is that fair or are we actually moving into mainstream?

• 38:41Speaker 00

  It's really started to shift. So last year, a hundred percent, it was like the year of the proof of concept. Everyone was sort of testing it out, playing around with it. But recently, There's been a big shift to urgency to get this tech into production. I think a lot of enterprises are scared of being caught flat footed. They've spent a year running P O Cs and testing stuff out. Now they're sprinting towards, I want to put this into production, transform my product, augment my workforce.

• 39:07Harry Stebbings?

  What's the number one use case for them in terms of what they need or want?

• 39:11Speaker 00

  The number one.

• 39:11Harry Stebbings?

  Cause it feels like everyone, everybody would be saying, hey, what's your AI strategy? And it's like, what does that actually mean? Is it Klarna who's very much, we want to optimize our customer service and we're going to do that. Is that the number one customer service? Is it employee augmentation and productivity?

• 39:27Speaker 00

  I think it's employee augmentation. It's these models becoming like a partner or a colleague to your entire workforce. That's the most popular use case.

• 39:36Harry Stebbings?

  I think Copilot's the right way to do that.

• 39:38Speaker 00

  I think Copilot is great and it's like the right idea of augmenting a workforce with a, with an assistant. But it's siloed again within an ecosystem. So it's plugs into Office and you know, the Microsoft suite of products. Enterprises don't just use Microsoft. They use Microsoft for their email and docs and spreadsheets. And then they use Salesforce for their CRM. They have SAP for their ERP. They have some HRM, they have internal software that they've built for themselves. And if you really want to augment the workforce, you need to have a, a platform for developing these assistants, these agents. That's agnostic to a particular toolset and that prioritizes the toolsets rationally across what people actually use, what the market actually uses.

• 40:21Speaker 00

  So I, I don't think that that's going to be done by Copilot.

• 40:24Harry Stebbings?

  You mentioned the word agent there. Agents is one of the kind of hottest topics in, in venture land. Do you think it's justified, the hype around agents, agentic behavior, what it does to workflows?

• 40:34Speaker 00

  I mean, the hype is justified a hundred percent. That's the promise of, of AI. The promise of these models is that they would be able to carry out work by themselves. That just dramatically transforms productivity. Once you have a model that can go off and do things independently over a very long time horizon. So no longer like, I'm going to do this one thing for you immediately in return and I'm done. But like over the next six months, I'm going to be pumping deals into your, your top of funnel. Or something like that, right? Like doing outbound for you. It just completely transforms what an organization can do. The hype is justified. I think my critique would be, is that work going to be most effectively done outside the model builders? Or within? Who's going to be best positioned to actually build that product?

• 41:15Harry Stebbings?

  Why would it be best done within the models first?

• 41:19Speaker 00

  Completely depends on the quality of the model. It entirely depends on the model. Like, the model is the reasoner behind the agent. And you have to be able to intervene at that level. If you're not able to actually transform the model to be better at the thing that you care about. If you're not the one building the model, if you're just a consumer of the model, you're structurally disadvantaged to build that product.

• 41:37Harry Stebbings?

  Who do you think is disadvantaged today? Everyone talks and is quite cynical about Salesforce. I'm like, I don't know. I wouldn't bet against Benioff.

• 41:45Speaker 00

  Yeah, I wouldn't either. He's amazing. And I think he's very cognizant of a threat against them. And I don't think he'll let it happen. So I don't see it going anywhere. The other thing is that you forget how sticky enterprise software is. There's not a lot of, like, mass displacement of enterprise software. It kind of just stays for decades. It's really hard to displace an enterprise software company.

• 42:05Harry Stebbings?

  Who do you think is disadvantaged?

• 42:06Speaker 00

  There's an opportunity for really transformative new consumer experiences. And consumers are far less loyal to one provider. They're going to go where their friends go. They're going to go where, you know, they get the best service, the best product. And so if someone can come along and provide something that is considerably better than what exists today using AI, consumers will move.

• 42:27Harry Stebbings?

  Who do you think has the best researchers?

• 42:29Speaker 00

  Other than Coher, I think it's quite distributed. It used to be very concentrated. It used to be like Google Brain, Google Brain, DeepMind.

• 42:37Harry Stebbings?

  Why were they so behind then?

• 42:39Speaker 00

  Well, they weren't in the sense that, like, two weeks after we released the transformer paper, we started training language models. So we, like, technologically and research-wise, Google Brain was certainly not behind. I think what's really important is product vision and the ability to imagine what could be with the technology. It's not just the technological development itself. It's the vision of what you can do with it. Even if you have people inside your organization who see that vision, are you equipped to enable them to execute on it? Or do they have to go somewhere else to execute on it? I think those are the questions that you have to ask. And then lastly, I mean, the scaling hypothesis, this idea that models will just continue to get better

• 43:21Speaker 00

  the more we pump into them and that we should be spending not just 10 times as much on building models, but 100 times, 1,000 times as much to build models. That's like a super risky, uncertain and crazy bet to make. But I definitely don't knock Google for the decisions it made. OpenAI made very, made good bets.

• 43:41Harry Stebbings?

  What did you think OpenAI's best bet was?

• 43:43Speaker 00

  The scale hypothesis for sure, like just that scaling is gonna sustain and that we should continue to 10x, 10x, 10x, 10x. So many people didn't believe in that. There was so much pushback on it, such a stupid, superfluous effort to go pursue. And they had the conviction to push through.

• 44:01Harry Stebbings?

  What do you think is the biggest thing that people are not seeing about the community right now in AI? And how we're looking at the next 12 to 24 months? And what are we getting wrong?

• 44:11Speaker 00

  I think there's, there's sort of like a meme that's going around of people saying we've plateaued, nothing's coming, it's slowing down. I actually really think that's wrong and not just from like a, we need to 10x compute and that type of thing perspective. And trust me, it'll get better, but from a methods perspective. So when I was talking about like reasoners and planners and models that can try things, fail and recover from that failure and carry out tasks that take a long time to accomplish. These are like for the technologists, obvious things that just don't exist in the technology today. We just haven't had time to turn our focus there and add that capability into the model. For the past year plus, folks have been focusing on that and it will be ready for, for production.

• 44:55Speaker 00

  So we'll see that come out and I think that will be a big change in terms of capability.

• 44:59Harry Stebbings?

  So for me as an investor, Aidan, help me, you're now an investor with 20 VC. Where's the opportunity for us?

• 45:06Speaker 00

  I think the product space, the application space is still extremely attractive. There will be new products that come out of this technology that transform social media. People love talking to these models. The usage time is insane.

• 45:20Harry Stebbings?

  Do you think this is good, Aidan? You grew up in a very wholesome, natural environment. You know, you mentioned your family also being in the UK. I'm sure you see them all now you're in the UK. I do not want my kids growing up in a world where they're speaking to agentic systems more than they are humans and like gaining fulfillment from speaking to a model.

• 45:39Speaker 00

  You might actually be wrong. I think you might want your children to be speaking to an extremely empathetic, extraordinarily intelligent and knowledgeable, safe intelligence that can teach them things and have fun with them and doesn't get tired of them, doesn't snap at them, doesn't bully them, doesn't pick on them, imbue them with insecurities. There is no replacement to humans. There's no replacement. There's no world where suddenly we all start dating chatbots and human birth rates plummet. I don't think that happens. I want to have a child. I can't do that with a chatbot, you know, like a human partner.

• 46:18Harry Stebbings?

  Yet.

• 46:19Speaker 00

  A human partner is way more, infinitely more valuable to me than whatever, like however compelling a chatbot is, a human is so much more valuable. It's the same reason why I don't think we'll be able to replace humans in the workplace. It'll be an augmentation. Humanity will become more productive and do more. It's not that there will be less humans doing the work. You can't replace humanity. Think about like sales, right? If I'm getting sold to by a bot, I'm not buying. It's that simple. I don't want to talk to a machine. Like for certain, like simple purchases, maybe, but for the purchases that count, the ones that matter to me and my company, I would want a human accountable on the other side of that, that deal. When something goes wrong, I need someone, a human, who has authority to, to be able

• 47:04Speaker 00

  to intervene. The fears around displacement and replacement, both on the consumer side where we're all going to get addicted to chatting to these chatbots and on the, the workplace, the end of work, you know, there's going to be mass unemployment. I can't see that happening.

• 47:18Harry Stebbings?

  I think there's always a recognition that there's always kind of this kind of mild displacement in new technology adoptions, which is kind of standard, but you do see some form of displacement, but not to the extent where we're like eighty percent of us are, I mean, I'm sure you look at your grandparents and so you stick your computer in there with email and they're like, what will we do all day? This is crazy. And so I completely agree with you there. I do worry on the lower end of the spectrum, though, being like a Klarna, losing whatever seventy, eighty percent of their customer service team.

• 47:43Speaker 00

  There will be localized displacement for sure, but in the aggregate, it'll be growth, not displacement. So for sure, there are certain roles that are vulnerable to the technology. It's kind of hard to come up with them, like customer support is definitely one, but at the end of the day, there still needs to be humans there to do that, just not as many as there are today. But customer support is a tough role, psychologically ugly. You get people screaming at you, like the reality of it, if you've ever listened in on all recordings of what it's like, that's a really emotionally taxing job. Every day you wake up, you go to work, you get screamed at and have to apologize for hours. That side of things, maybe we let the models handle those conversations and the humans can come in and help with,

• 48:27Speaker 00

  you know, the actual customer support conversations that humans would enjoy dealing with. They have a problem that needs solving and they're not angry about it. There's just an opportunity to make this person's life better.

• 48:36Harry Stebbings?

  What does AI not do today that you think it will do in three years, will be completely transformative?

• 48:41Speaker 00

  I think robotics is like the place where there will be big breakthroughs. The cost needs to come down, but it's been coming down. And then we need models that are much more robust, just because a lot of the barriers have fallen away. Like before, like reasoners and planners inside of these, these robots, like the software behind them, they were brittle and they had to like, you had to like program each task you wanted it to accomplish. And it was super hardcoded to a specific environment. So you have to have a kitchen that is laid out exactly like this.

• 49:10Harry Stebbings?

  Exactly the same dimensions, nothing different.

• 49:12Speaker 00

  Yeah. So it's very brittle. And on the research side, using foundation models, using language models, they've actually come up with much better planners that are more dynamic, that are able to reason more naturally around the world. I know this is already being worked on, this is like thirty. Humanoid robotic startups and that type of thing. But soon someone's going to crack the nut of general purpose humanoid robotics that are cheap and robust. And so that will be a, that'll be a big shift. I don't know if that comes in the next five years or 10 years. It's going to be somewhere in that range.

• 49:44Harry Stebbings?

  And I could talk to you all day. I want to do a quick fire round. So I say a short statement, you give me your immediate thoughts. Does that sound okay?

• 49:50Speaker 00

  Yeah, yeah, let's do it.

• 49:51Harry Stebbings?

  What have you changed your mind on most in the last 12 months?

• 49:54Speaker 00

  The importance of data. I underrated it dramatically. I thought it was just scale and a lot of proof points have happened internally at Cohere that have just transformed my, my understanding of what matters in building this technology.

• 50:08Harry Stebbings?

  So now is the quality of data.

• 50:10Speaker 00

  Yeah, quality, like a single bad example, right? Amongst like billions, it's so sensitive. It is a bit surreal how sensitive the models are to their data. Everyone underrates it.

• 50:21Harry Stebbings?

  How much money have you raised now?

• 50:23Speaker 00

  In total, about a billion.

• 50:25Harry Stebbings?

  Fucking hell.

• 50:26Speaker 00

  I know, yeah.

• 50:26Harry Stebbings?

  That's a lot, that's a lot of money. What was the easiest round to raise?

• 50:31Speaker 00

  Maybe the first one.

• 50:32Harry Stebbings?

  What was that, the fastest as well?

• 50:33Speaker 00

  Yeah, it was kind of like a conversation and here's a few million bucks. Give it a try. So I think that one was probably the easiest. When you're trying to raise half a billion dollars, it's a little bit more involved and.

• 50:44Harry Stebbings?

  Do you slightly pinch yourself when you see $500 million going in an account? Cause I manage funds today, but we get capital calls. And so it's not like here's 500 million. It's like you call it over several years and you just get.

• 50:57Speaker 00

  Yeah, yeah. And the interest on it is fantastic.

• 51:00Harry Stebbings?

  Yeah.

• 51:01Speaker 00

  Yeah, I do pinch myself.

• 51:02Harry Stebbings?

  The interest on that is 25 million a year.

• 51:05Speaker 00

  I don't know what the specific number is, but it's, it's a lot. It's a big number. My brain is broken. Cohere has broken my brain when it comes to economics and money. Half a billion does not feel like a lot, like relative to my competitors, it's not a lot.

• 51:17Harry Stebbings?

  Does that worry you?

• 51:18Speaker 00

  No, I mean, it's part of our strategy. Like if we wanted to go take that deal, we could go take that deal. But our strategy has been to pursue independence and doing this ourselves.

• 51:26Harry Stebbings?

  If you can have any board member in the world, who do you have and why them?

• 51:30Speaker 00

  Mike Volpe and Jordan Jacobs, my existing board members.

• 51:33Harry Stebbings?

  Why is Mike such a good board member? Many people say this.

• 51:35Speaker 00

  Yeah, Mike's, Mike's incredible. It kind of feels like he's seen it all before. Like I can come to him with virtually any problem and he's encountered that three times before. And the first time it went like this, the second time it went like that, third time it went like that. He just has such good experience and advice.

• 51:51Harry Stebbings?

  Jeff Hinton, Jan LeCoon, which one's your boy?

• 51:55Speaker 00

  Definitely more Jeff. I have a closer personal relationship with him than Lukin, for sure.

• 52:01Harry Stebbings?

  Do you think Jan is too optimistic?

• 52:03Speaker 00

  No, I'm way more aligned with Jan and his beliefs about AI. Jeff is like very doomsday, pilled, thinks that this technology is going to destroy the world. Jan is much more optimistic and I'm aligned in that direction. I think that unfortunately Jan has kind of become an Elon reply guy. I think Jeff is, my co-founder Nick, he's super close with Jeff. They play chess every Monday. Jeff is so, so smart, so intelligent and so thoughtful, such a deep thinker. I admire him more than almost anyone in the field.

• 52:35Harry Stebbings?

  You have teams in London now. You live in London. Everyone talks about the death of Europe. You know, I had the wonderful Delian from Founders Fund on saying that Western Europe would be a third world state or kind of collection of countries soon. And negativity is quite real here, it feels. How do you feel now building incredible engineering research teams in London and Europe?

• 52:55Speaker 00

  England stands out from the rest of Europe. There's a technology optimism that exists here and a willingness to invest and make the changes necessary to support developing an ecosystem. In Europe proper, by the way, my mom is British, my dad is Spanish and I have both citizenships. So I'm also very much European, spent my summers there, like family is there. Unfortunately, the culture is just hostile towards tech. It's hostile, like the solution to tech is regulation in the European mind. I think there's pressure to change though, and France is becoming much more ambitious and making a lot of noise on the European stage as well as the global stage about we need to be more progressive. It might take a decade though.

• 53:37Harry Stebbings?

  In person or remote?

• 53:39Speaker 00

  So here was like born in the pandemic and so we're totally remote. We're all over the place, not totally remote. We have offices in Toronto, London, New York SF. Those are definitely the centers of mass of the company.

• 53:50Harry Stebbings?

  And people come in every day?

• 53:51Speaker 00

  Yeah, in person is just so much better. You can't even quantify the productivity lift from in-person work.

• 53:58Harry Stebbings?

  What question are you never asked that you should be asked? Final one.

• 54:02Speaker 00

  I don't think I get asked enough, where do you want things to go? Like I get asked a lot, where will things go? I get asked a lot about the downside risks of the technology. There's so much fear in people's minds when they think about AI and so little discussion about the opportunities we have. And I don't think people talk about that nearly enough.

• 54:23Harry Stebbings?

  Where do you want it to go?

• 54:24Speaker 00

  I think that the world is super supply constrained. And pretty much every luxury we have today has come from technology that has developed to increase productivity, boost the supply of things, make them more abundant, make them cheaper. And so what I really care about with this technology is driving productivity for the world and making humans more effective, able to do more. I think it's so unsexy. Productivity is just like so underhyped.

• 54:52Harry Stebbings?

  But if you apply like a 5% productivity gain to the NHS, which is obviously our healthcare system here, that is a seismic needle moving shift to the state of the country, the state of the country's budgets, the healthcare in this country, millions of people's lives every day.

• 55:06Speaker 00

  Like in in Canada, real GDP hasn't really been increasing. Someone called it the like the lost decade. People aren't getting wealthier. Things are not becoming more abundant. You can't afford more for a decade. And so that sort of stagnation, you start to get a lot of social turmoil. Things start to be, it's not a growing pie, it's a fixed pie that you have to fight for your slice of. And I think those dynamics really concern me. Our priorities as a society right now should be on productivity and growth.

• 55:35Harry Stebbings?

  Listen, I've loved doing this. Thank you so much for putting up with my at times rather base questions on rags and flops and at times prime questions on fundraisers, but you've been fantastic.

• 55:44Speaker 00

  Thank you so much. This is so fun.

• 55:47Harry Stebbings?

  I mean, shows like that are just why I love what I do so much. Getting to speak to the most incredible people at this moment in time is just fantastic. If you want to see the full episode, you can check it out on YouTube by searching for 20 VC, that's two zero VC. But before we leave you today, when a promising startup files for an IPO or a venture capital firm loses its marquee partner, being the first to know gives you an advantage and time to plan your strategic response. Chances are, the information reported it first. The information is the trusted source for that important first look at actionable news across technology and finance, driving decisions with breaking stories, proprietary data tools, and a spotlight on industry trends. With a subscription, you will join an elite community that includes leaders from the top VC firms, CEOs from

• 56:33Harry Stebbings?

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• 58:03Harry Stebbings?

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