Cohere

“We were surprised by cohere recently. They did came up with new good models. And I think that was a surprise for us.”

“I don't want to name names, but let's say Coherit 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 going to be changes in the product landscape and what we offer to sort …”

“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.”

“It's mostly trust. In the technology. So security. Everyone is very sketched out by the current state of things.”

“The reality of the matter is there's no market for last year's model.”

“Especially with enterprises, they never let you train on their data. And so we can't train on any of our customers' data.”

“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 scar…”

“We're an economically rational actor, and so 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.”

“Cohere. But other than Cohere, I think it's quite distributed at this stage. It used to be very concentrated. It used to be like Google Brain.”

“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, it plugs into Office and, you know, the Microsoft suite of products, but enterprises don't just use…”

“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.”

“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.”

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

“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.”

“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?”

“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,…”

“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.”

“Oh, yeah, we have at times. Yeah.”

“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…”

“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.”

“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.”

“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.”

“Yeah. 6.8.”

“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.”

“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…”

“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…”

“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…”

“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 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…”

“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.”

“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.”