Cohere

“So they are going to the direction of highly tailored sovereign AI models for the domestic market. And we actually see this happening more and more. So for Cohear, they will service the Canadian market. For the US market is going to be served by OpenAI Entropi…”

“And over the course of my time at Section one 32, we've had six funds. We've invested in companies like CrowdStrike and Cohere and Coinbase and all six of those funds have averaged about four hundred million in size, and all are performing in their top decile,…”

“They didn't take investment from any of the hyperscalers. They had opportunities to do that, but they chose not to. So give the user and the enterprises a choice of any cloud. Rather than be tied to one cloud.”

“I tend to actually place my bet not on the fact that we're going to reach like a single super intelligent agent, but on the fact that we are much more likely to live in a future where there's going to be many agents for many things. And so some agents will abs…”

“In reality, paying customers want like a good trade-off in terms of performance for efficiency. So, you know, we'll train bigger models, but we'll deploy smaller models because it gives us that trade-off. It's like good enough intelligence to get the job done.”

“And that's why, honestly, for many years I've been so much an advocate for open science. I just don't believe that you can keep these ideas boxed in unless you're willing to keep people boxed in, which we are not willing to do. And so I don't think we have a w…”

“The other way to think about sovereignty that we're hearing a lot is that companies want a robust plan for AI. And so, you know, they want options. They may be using one model, but they actually want to have another model to be able to compare, to benchmark if…”

“But, you know, there, for example said that we were, that we only sampled, like, nine percent open source models and, like, you know, 60%, like, closed source models, and this created a gap between open and closed source. But in reality, we're actually really …”

“The more that you can integrate agents and multi-agents Across various functions within your companies, the greater ROI you're going to get, and I think that proves out at a smaller company.”

“Our slogan is ROI over AGI. As long as our business customers are getting an ROI, that is what we're focused on.”

“One of the things that Cohere is doing is actually to develop AI models that run on premise. So that means enterprise bring it in, they run it locally, so the company has to worry about the training Of the models. Obviously we want world-class models for the n…”

“I think one of the biggest challenges, the fact that it's very hard to have predictability, right? Everyone wants to know when are we going to hit the breakthrough? Everyone wants to know how many GPUs do I actually need? Everyone wants to know, like, what's t…”

“Compute and data have a more linear effect on progress. You build more compute. You run bigger models. You can typically get better performance. You feed in more data. It's not just quantity. You need to worry about quality and diversity as well, but roughly i…”

“They've been, the scaling laws have been remarkably robust. They don't play exactly as we expect, but still, they've been remarkably robust. Lots of people have bet against scaling laws in the past. And I would say overall, you know, we've seen a pretty, prett…”

“I don't think it becomes that productive to put a bunch of AI superstars all together in a room without the execution machine, without the social glue.”

“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. There's a lot of code that will get thrown away, but wait another 10 years and I think the quality of th…”

“We build AI systems that go into enterprise. None of our clients ask about, like, are you able to win the math Olympiad with this model? That's not what they care about. They care about bringing value to their business.”

“Maybe there's like 10 companies in the world that are building, like, large language models. In the, yeah, maybe the, in, maybe there's like 15 in, I don't know, we'll have to figure out how, there's a few that have popped up recently. But there's some number,…”

“But the algorithms I think are not the bottleneck in terms of making those models more useful. I do think a lot of it is still getting good quality data and then making good quality synthetic data From your good quality real data.”

“And if you want to make the best model for a given, you know, interface, it's best to be training the model on that interface.”

“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, you know, taking in like a grid of pixels and based on rules, predicting the next one. That's not a thing any of our customers ha…”

“Our model command day and the command day reasoning model, which we just released. Command A Vision model, which we just released. Those, they're all trained to fit on two GPUs.”

“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.”

“Yeah. 6.8.”

“Oh, yeah, we have at times. Yeah.”

“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 for us, right?”

“Right now? No, no, no, no. Cursors built a good product. Like, they built, you know, they've done really good UX stuff, and it's cool.”

“We're in Cohere, and in and we have a seed investments in Mistral.”

“Synthetic data is incredibly effective. It's now the majority of the data that we train on for creating something like command A.”

“Command command a isn't currently multimodal, but you can imagine Very soon it will be.”

“So we can take something that used to be a month. And bring it down to, you know, four hours, eight hours.”

“But with, you know, a company like Oracle, which has all of this workplace software in fusion apps and NetSuite, they've implemented hundreds of use cases themselves using Coheres models.”

“One from Reuters was that Cohere scales to a hundred million in revenue annualizes in May, Seemingly positive, exciting number, but then from the information is that Cohere, that basically they had shown investors they'd be making four hundred fifty million AR…”

“Coherent to this very extensively in their latest flagship model, command A”

“The thing that's most fascinating to me is all these model companies, Mistral Anthropic, Cohere that really don't publicly have these capabilities. Where now anybody employed at those companies basically shouldn't sleep. For the next, however long it takes to …”

“Synthetic data, it doesn't have to come from a single model. And because we have so many good models now, you could like pull these models together and get like a dataset that's over really high quality and that's diverse and that's covers all your needs.”

“We also use a lot of other models. We change that on a regular basis, and we've invested in a lot of model companies, including Anthropic and Mistral and Cohere and many Many of them.”

“So in this paper here by Kohir, what they did was they have a reference model, and this reference model is GPT-IV. And then essentially what they did was the ensemble command R, Haiku, and GPT-IV. And I can't remember what the I think the ensemble was just maj…”

“We're not going to build a ChatGPT competitor. What we want to build is a platform and a series of products to enable enterprises to adopt this technology and make it valuable.”

“What we've pushed organizations to do is have a strategy that encompasses that full pyramid. Yes, you need the generalist standard stuff. Maybe there's some industry specific tools that you can go out and buy, but then if you're building, don't build those thi…”

“What we've seen is today you can build a model that's as good as GPT-IV in all the things that enterprises might care about. For ten million dollars, twenty million dollars, like just orders of magnitude less than what was spent to develop that model. And so i…”

“So that's the strategy is don't lead. Don't burn, you know, three, five, seven billion dollars a year to be at the front, be six months behind. And offer something to market to enterprises that actually fits their needs at a price point that makes sense for th…”

“Taking those models and trying to fine tune them It's just, it's not as effective as building it yourself and you have much fewer levers to pull than if you actually have access to the data and you can change the data that goes into that process.”

“At some stage we're gonna have to give these models the ability to run their own experiments to fill in areas of their knowledge that they're curious about. But I think that's quite Quite a ways away. And it's going to be tough to scale that. It will take many…”

“We will build AGI if what you mean is very useful, generally capable technology that can do a lot of the stuff that humans can do and flex into a lot of different domains. If what you mean is, you know, are we gonna build God? No.”

“I don't think that models are actually getting commoditized. I think what you see is you see price dumping. And so you see people giving it out for free, giving it out at a loss, giving out at zero margin. And so they see the prices coming down and they assume…”

“I think in reality, the state of the world is there's a total technological refactor that's going on right now and will last the next 10 to 15 years. And it's kind of like we have to repave every road on the planet. And there's like four or five companies that…”

“I don't know about God. Like, I don't think I'd ever use that term to describe what's coming. I think we're going to have some really powerful and useful tools. Emerge. I think that's what's coming. The idea that we're building AGI or something that's just gon…”

“I expect them to be far more robust, reliable and I expect that they'll just be more capable. There's lots of things today that models break in unsupp, in surprising ways, and I think that's going to start to become rarer and rarer. We're going to be able to p…”

“I don't see a step change coming, but I see a steady continuous course towards very high accuracy, very high reliability AI.”

“Even if, you know, like just as a hypothetical even if the technology froze and what we have today is all we get, there's so much good to be done. There is so much work to go do. To implement this technology across the economy really boost productivity, drive …”

“My personal perspective is that, you know, building a massive model, it's not actually useful for the world if it's too big to be consumed, if it's too expensive to actually deploy.”

“I don't think within any achievable scaling up for humanity that will reach that tipping point. It just saturates the gains become much, much smaller. And so you're much less willing to want to pay double the price for a minute difference. But it is pretty con…”

“We're kind of at that level where we're currently hiring PhDs to teach the model in their specific domain.”

“More and more synthetic data is becoming a huge chunk of the data that we train on.”

“I actually take, I think the less popular view, which is The internet is enough, and by observation, you can actually learn enough to be extremely, extremely compelling. I think that's if we're talking about AGI and doing things as well as humans do, I think t…”

“So there's some good examples of that with our partner Oracle, which they have this suite of applications, which basically power enterprise, HR, supply chain all of these sorts of back office functions. And we're powering over 50 different applications within …”

“Maybe this stuff feels banal. Maybe productivity feels boring compared to some of the hype of AI, but it is the value. This is what we're trying to build for.”

“I am not aware of it. I don't think I have any example of that happening. It's very assistive actually. So it's less about replacement. It's more about augmentation. Like at the moment, what everyone's building are tools to augment their workforce to make them…”