The Exchanges

Q Do you think governments are good at setting the standards? When you look at AI and where we're at, and then when you look at the sophistication levels of government, um, programs or decision makers, with respect, they're just a little bit behind. Do you think they are actually equipped?

A I don't think you should look at where government are in terms of necessarily AI regulation. AI as a field is so incredibly young and fast moving and, and by nature, and there's some good in this. Governments are moving a little bit more cautiously and, and, and usually need to benefit from our knowledge to make good policies. Um, and so I, I do think you, you can look at other fields in terms of, of regulation. You know, you look at, um, uh, aviation. The security record for aviation today compared to where we were 50 years ago is just incredible, and governments have played a role in defining that in terms of standards and in terms of what are the, the norms and so on. So, I'm quite hopeful, I'm an optimist about this, maybe it's my Canadian side, that governments can play a useful role, um, in many cases, you know, clear standards Actually means reducing uncertainty for a lot of companies in this space, but we shouldn't expect that to be ahead of the technology. I think that would be the wrong order of things in some sense. We need to develop that technology with enough of a creative space, and we need to learn fast and then develop the right, the right guardrails for that technology from, from, from the real earnings we have.

AI assessment note: “I'm an optimist about this... that governments can play a useful role”

Q the benefits of not being an American company, um, given some geopolitical challenges sometimes. Um, I'm just intrigued. Do you think we will have these kind of sovereign models for each geo? You know, we have Mistral in France, you know, coheres obviously in Canada or founded in Canada, but I know you've got global kind of HQs. Do you think we will have these sovereign models and regionalized winners?

A I do think it's healthy that there are models that are getting built in different places around the world, not just in the US and China right now. I think this is healthy in terms of diversity of, of thoughts. I think it's healthy in terms of having a greater amount of people with access to technology. I do think for Cohere, you know, the vision isn't to be a Canadian company. Like, the vision is to be a global AI company, and I think Yes, you know, we have a headquarter in, in Toronto. We have teams that are distributed around the world. We have a great team here in London, as well as in the US and in France and other places. And so, you know, having that ability to deploy models that operate across the world, I think it's going to be absolutely an important part of the, the strategy for Cohere. I think there's a great opportunity. Um, what it gives us to be headquartered in Canada is like a Sensitivity to the fact that it's not always a one size fits all solution. You know, I go back to the research we've done. Um, we've done leading work in terms of multilingual model. Um, And it turns out it matters. You go to Japan, you go to Korea, and they do want models that work well in their language. People in the workforce are still operating in, in the language of the country. So having a company that, that is attuned to that, that values that, that internationalization of model is…

AI assessment note: “I do think it's healthy that there are models that are getting built”

Q It's funny, this conversation has changed a lot of kind of previously held assumptions for me. When you think about what you did believe that you now have changed your mind on, what's most prescient?

A I'm a scientist that is happy to be proven wrong anytime, as long as there's new evidence. I'm genuinely curious to know. Other scientists are much more like holding on to very, very strong conviction. I have weak conviction, but very strong respect for the scientific method and, and, and rigor, experimental rigor, you know, theoretical rigor as well. Um, so there's a ton of things. I mean, I, ah, I mean, I used to be quite skeptical that neural networks were necessarily the ultimate solution to machine learning. I'd seen enough cycles of neural networks kind of peaking and, and, and then, um, being less, less useful, and I used to think every time you change the scale of the data, you know, you go from hundreds of examples to thousands, thousands, to hundreds of thousands, to millions of examples. Every time you change the size paradigm that neural networks were the first thing we tried, Because they're a universal function approximator. And then something else comes out that was better. And that was true for the previous generations. You know, some of you may remember SVMs as like being better than the neural networks in, in early 2000. And I, I seem to be quite wrong on this one. Like neural nets seem to be here to stay. Um, and the ability to do back propagation and gradient descent and all that seems to be a really powerful way to learn.

AI assessment note: “I used to be quite skeptical that neural networks were necessarily the ultimate solution”

Q Do you worry that we are creating a world with just much worse code? A lot of people are concerned by the quality of code that's being outputted and actually how we're just relying on it pretty haphazardly. Do you worry about that?

A Let me make an analogy in terms of, like, the quality of generation. You know, you ask about code generation, but let me take you back to 20 15 and image generation. I don't know if you have it in your mind, but the quality of the images that were generated, we had image generation models in 2015. They were really bad. The resolution was bad, the composition was bad, and so on. And from 27, 2015 to about 20, 22 or so, we saw huge progress in terms of the quality of the image generation. So you think of code generation, like right now we're in the phase we were for image 10 years ago. Yes, there's a lot of bad code that's getting generated. Um, there's a lot of code that will get thrown away, but wait another 10 years and I think the quality of the code that's produced is going to be excellent.

AI assessment note: “wait another 10 years and I think the quality of the code that's produced is”

Q Do you think governments are good at setting the standards? When you look at AI and where we're at, and then when you look at the sophistication levels of government, um, programs or decision makers, with respect, they're just a little bit behind. Do you think they are actually equipped?

A I don't think you should look at where government are in terms of necessarily AI regulation. AI as a field is so incredibly young and fast moving and, and by nature, and there's some good in this. Governments are moving a little bit more cautiously and, and, and usually need to benefit from our knowledge to make good policies. Um, and so I, I do think you, you can look at other fields in terms of, of regulation. You know, you look at, um, uh, aviation. The security record for aviation today compared to where we were 50 years ago is just incredible, and governments have played a role in defining that in terms of standards and in terms of what are the, the norms and so on. So, I'm quite hopeful, I'm an optimist about this, maybe it's my Canadian side, that governments can play a useful role, um, in many cases, you know, clear standards Actually means reducing uncertainty for a lot of companies in this space, but we shouldn't expect that to be ahead of the technology. I think that would be the wrong order of things in some sense. We need to develop that technology with enough of a creative space, and we need to learn fast and then develop the right, the right guardrails for that technology from, from, from the real earnings we have.

AI assessment note: “I'm an optimist about this... that governments can play a useful role”

Q the benefits of not being an American company, um, given some geopolitical challenges sometimes. Um, I'm just intrigued. Do you think we will have these kind of sovereign models for each geo? You know, we have Mistral in France, you know, coheres obviously in Canada or founded in Canada, but I know you've got global kind of HQs. Do you think we will have these sovereign models and regionalized winners?

A I do think it's healthy that there are models that are getting built in different places around the world, not just in the US and China right now. I think this is healthy in terms of diversity of, of thoughts. I think it's healthy in terms of having a greater amount of people with access to technology. I do think for Cohere, you know, the vision isn't to be a Canadian company. Like, the vision is to be a global AI company, and I think Yes, you know, we have a headquarter in, in Toronto. We have teams that are distributed around the world. We have a great team here in London, as well as in the US and in France and other places. And so, you know, having that ability to deploy models that operate across the world, I think it's going to be absolutely an important part of the, the strategy for Cohere. I think there's a great opportunity. Um, what it gives us to be headquartered in Canada is like a Sensitivity to the fact that it's not always a one size fits all solution. You know, I go back to the research we've done. Um, we've done leading work in terms of multilingual model. Um, And it turns out it matters. You go to Japan, you go to Korea, and they do want models that work well in their language. People in the workforce are still operating in, in the language of the country. So having a company that, that is attuned to that, that values that, that internationalization of model is…

AI assessment note: “I do think it's healthy that there are models that are getting built in different places”

Q Do you worry that we are creating a world with just much worse code? A lot of people are concerned by the quality of code that's being outputted and actually how we're just relying on it pretty haphazardly. Do you worry about that?

A Let me make an analogy in terms of, like, the quality of generation. You know, you ask about code generation, but let me take you back to 20 15 and image generation. I don't know if you have it in your mind, but the quality of the images that were generated, we had image generation models in 2015. They were really bad. The resolution was bad, the composition was bad, and so on. And from 27, 2015 to about 20, 22 or so, we saw huge progress in terms of the quality of the image generation. So you think of code generation, like right now we're in the phase we were for image 10 years ago. Yes, there's a lot of bad code that's getting generated. Um, there's a lot of code that will get thrown away, but wait another 10 years and I think the quality of the code that's produced is going to be excellent.

AI assessment note: “Yes, there's a lot of bad code that's getting generated... but wait another 10 years”

Q To now also building product. Is there ever this, like, inherent conflict between intellectually interesting research with the need to productize and monetize, and how do you think about that?

A I mean, one of the reasons I'm really excited to be joining Cohere actually is because like we're at a stage where AI is really starting to be useful, maybe not as useful as people think it is, but we are there. Um, and by working on AI that's going into enterprise, I feel we're going to get such an interesting signal of what works and what doesn't work. You know, we keep on talking about, you know, AGI and AI for the masses and so on, but Actually, like when you need to sell AI to a business, you get a real signal of what works, what doesn't work. Um, and that's what I'm most curious to see. Um, and you know, we've been using these academic benchmarks for many years. You get some signal, but it's not the same as, as getting this to do productive work. Um, so I'm curious to, I'm curious to learn out of that, you know, we're going to get new types of data. We're going to get, I think, a lot of insights That are then going to drive the research ideas. Um, I think that's, that's the other thing to think through when you have a large space of ideas to explore. Getting that feedback signal from the real world is super useful to guide you through that search of ideas.

AI assessment note: “getting that feedback signal from the real world is super useful to guide you”

Q How have you seen enterprise reaction to this? There's fear from workers sometimes, there's excitement from leaders, there's apathy sometimes. How have you seen and measured enterprise response?

A A lot of the, the workforce can be reasonably fearful about job displacement. Um, there's a lot of also individuals who have, you know, a bit of an instinctive reaction to, to change. And, and change can be hard for a lot of people, and we're seeing a lot of change in a very short time span. And so, I think there's also a generational effect. I think for some generations, there's, The, that, that change is, is more, more jarring. I think for the younger generations, teenagers, young adults at home for them, yeah, it's just native. They just, you know, kind of, you know, gonna grow up with that technology in a different way than, than some of the older generations.

AI assessment note: “A lot of the, the workforce can be reasonably fearful about job displacement.”

Q How have you seen enterprise reaction to this? There's fear from workers sometimes, there's excitement from leaders, there's apathy sometimes. How have you seen and measured enterprise response?

A A lot of the, the workforce can be reasonably fearful about job displacement. Um, there's a lot of also individuals who have, you know, a bit of an instinctive reaction to, to change. And, and change can be hard for a lot of people, and we're seeing a lot of change in a very short time span. And so, I think there's also a generational effect. I think for some generations, there's, The, that, that change is, is more, more jarring. I think for the younger generations, teenagers, young adults at home for them, yeah, it's just native. They just, you know, kind of, you know, gonna grow up with that technology in a different way than, than some of the older generations.

AI assessment note: “teenagers, young adults at home for them, yeah, it's just native.”