Hey everyone, big tech just posted blowout earnings, but Meta and Microsoft, they are falling anyway. As AI spending eats into their balance sheets on big tech's biggest earnings week of the year, the market just told us what it's willing to pay for. It is Thursday, April 30, and I'm Dear Dosa. Let's get into it. Cloud growth at the hyperscalers, it is booming, and it's making the bull case for the AI trade at large. Google Cloud grew 63%. AWS had its best quarter in more than three years. Microsoft's AI business, which runs through its Azure cloud, it doubled year-over-year. Everyone is saying the same thing. They're compute constrained and they just can't build fast enough. But there's another side to all of this. The cost of that growth is showing up in the cash flow statements and today investors they're noticing the combined capex commitment across the four mega caps that reported last night it's roughly $700 billion. Now that money has to come from somewhere and it's coming out of free cash flow. Amazon's free cash flow down 95% in a year. Microsoft's gross margin hit its lowest level since 2022. and its CFO said data center depreciation will compress margins for several more quarters. Then you got JP Morgan today downgrading Meta. Their model now shows negative free cash flow of 4 billion this year and negative 24 billion next year. Now for years these balance sheets they were big enough that the buildout barely dented them. Not so much anymore. And the question for all of them is whether the revenue shows up before the cushion runs out leaving very little room for error. Now, here's why that could start to matter a lot more. Something else happened this week that got a little bit lost in the earnings noise. Deepseek released its latest model V4, and it's as good or nearly as good as the latest from OpenAI, Anthropic, Google on coding, agentic, knowledge work, but and this is key, it runs at a fraction of the price. Now, the market barely blinked on this news, and that tells us something, cheaper, nearly as capable models, they're the new normal. They're being served by the cloud giants alongside the American frontier models. So every dollar of that $700 billion in AI buildout. It's going to defend a pricing model that is quite frankly getting harder to defend on the biggest AI traffic aggregator, open router, Chinese models, open source models. They were barely used a year ago. Today they make up most of the traffic and three of the top five are Chinese. So, if you are a Fortune 500 buyer looking at rising cloud or token bills, you might be asking a new question. Is the premium AI actually worth it? Now, the person that I really want to put this question to is Aidan Gomez, founder and CEO of Cohere, a foundation model company tailored for the enterprise. So, smaller, more efficient models that customers can even run on their own servers. Aidan, welcome. I've been really eager to have you on the live stream, so I appreciate you making the time.
Yeah, thanks for having me. Happy to be here.
And as always for our audience that's following along, keep your comments and questions coming in and we'll get some of them to Aidan and to Daniel Newman later. Aidan, let's start where I left off. I just asked a question that I feel like covers so much of where we're seeing the AI trade today and where it's going. Is premium AI, Frontier AI, is it still worth it?
I think you can see in the demand that people are willing to pay and you can see in the level of capex spend and the reduction in free cash flow that companies can see the future demand ramping. So yes, people will continue to spend to get access to extremely high quality models. In terms of the enterprise and how their adoption of AI is being shaped, two of the core bottlenecks to that are cost and security. And so trust is really deciding who they're going to choose. And to your point about DeepSeek V4, I think that's an example where for governments, for regulated industries, critical industry, they're not going to be using and leveraging that technology. Chinese models just simply aren't an option. And so organizations that need to trust the models, the decisions that they're making, the code that they're writing are going to be willing to pay a premium to access western democratically aligned technology.
Talk a little bit about Cohere's business model then because my understanding you guys serve the enterprise and you're a model builder. How are you thinking about it and the cost payoff and benefits?
Yeah, absolutely. So we build our models from scratch. Our business is exclusively on the enterprise side. So we focus in particular on the high security settings. So think grid operators, financial services, telco, government. And in those settings, the data and the systems that these models are accessing are really national security concerns. And so there's just no way that there's going to be a reliance on a non-democratically aligned technology stack. And what Cohere does uniquely well among all the labs is very secure deployment. So whether it's on prem or completely airgapped, we can deploy in a way that there is no cyber risk. We can deploy inside a customer's data center. We can deploy into a submarine a kilometer under the surface of the ocean with no ability to talk to the internet. And so that level of security just gives us a completely unique value proposition. It also presents constraints. We have to deploy on extremely limited compute footprints. And so we talk about how these large models are driving massive buildout and spend for the hyperscalers but for these high security settings they can't get their hands on enough chips either. And so we need to be able to deploy on two to four GPUs and so that presents a completely new technological constraint that basically rules out massive massive models. And so we focus on something that is right sized for the market that we serve.
Right. It you know two of the biggest frontier labs Open AI and Anthropic certainly turning their attention to security with Mythos and you know Sam Altman Open AI were out with you know big plan last night. How do you compete with those ones? And those bigger models you know Mythos the whole narrative surrounding it is it's so powerful they can't release it. When they do, how does Cohere fit into that picture?
Yeah, so the massive models, you know, rumor is that it's something like 10 trillion parameters in scale that does not fit on two to four GPUs and so it's not going to be served inside of these extremely compute constrained environments. So we don't really run into those models when we're serving the market. What I will say is that these massive models that are capable of very sophisticated cyber offense and defense use cases are a very interesting new capability that exists in the world. We can now at scale find exploits inside very sensitive software. And so it reinforces the significance of private deployments, ensuring that our grid operators aren't putting the code that operates the power that flows into all of our homes, all of our businesses in a place that it could be accessed by a third party. Where that third party could use AI models to find exploits that they could use to shut off the grid or the same thing in financial services or in healthcare etc. If we expose the infrastructure, the software that powers our economy, whether it's in finance, whether it's in healthcare, whether it's in energy, the risk now that someone will be able to exploit vulnerabilities and use them against us is higher than it's ever been. So I think the notion of private deployment is essential. We've known that these cyber capabilities were going to emerge in models for a while. And as we've started to see software increasingly be written by these large language models instead of by humans, you're going to care a lot about which models you're using, whether they're like who they're coming from essentially. So, if you're going to be replacing your entire software engineering team with models that are writing your code, you probably don't want that coming from China because it might be subtly introducing vulnerabilities that you're not going to catch. And so the trust barrier that I was describing, the trust barrier to enterprise adoption continuously gets higher and higher.
Right. And it sounds like, you know, Cohere's models have a very specific use case when you can have a model on prem and you need the utmost security. When you say though that sort of the whole So I think also what you're talking about is sort of the back door, right? That's been a concern with these Chinese models. You can self-host them, but you don't know if there's a back door that can get into your data. Why then does AWS, the hyperscalers, Michael Dell, why are they hosting and promoting these open source Chinese models?
Well, for certain applications, I think they're great, right? Like in low sensitivity settings, it's pretty reasonable to use a Chinese model. I think for startups that are looking for the lowest cost option and aren't operating at scale yet, it seems like a reasonable and very helpful tool. Of course, I think everyone would prefer to be using a democratically supported and aligned piece of technology. So, I hope there is and Cohere is contributing to much more democratic open source. We want to continue to put that out there. But you know there is a market for these and they are cheap as you say and for the less secure settings maybe there's a place for them.
Right and you know you see sort of usage go up and we hear all the time about how compute constraints so is that calculation for enterprises changing especially if you're not in an industry that requires so much security is that calculation changing especially because some of these open source models are getting very close to the frontier and that sort of lag is closing.
Yeah. Cost control and then the other thing is like the compute bottleneck, right? So there's simply not enough compute to support using these massive models. So we need efficient small models that are good enough for the use cases that people are pursuing. Otherwise we're just not going to have sufficient compute to meet demand. So there is a shift in the market. I think everyone over the past year has been racing to, you know, at all costs adopt AI. There's going to be another phase that we enter into where CFOs are looking at the expenses being spent on some of these models. They're going to try to optimize. They certainly aren't going to pull back and it's going to continue to grow, but we're going to need to find ways to use smaller models, more efficient models. I hope that doesn't mean shifting over to a Chinese tech stack to satisfy that. I think there's companies like Cohere and others who are building you know more aligned technology that satisfies that efficiency need.
Right but it does sort of beg the question are the American models better than Chinese ones like what is the gap is DeepSeek V4 a real threat especially when we see sort of it matching or surpassing some of the frontier ones on the benchmarks.
I think it's very close. The gap between the two countries in addition to Canada, in addition to France, Germany, it's closing rapidly.
Right. And you're saying that there's options. I mean, Cohere's working on it, Nvidia's working on it, the startup Reflection is working on open source. Is there enough companies working on it in America? And what happens to OpenAI and Anthropic if they aren't working on this sort of open source model race?
I think there's a lot of folks contributing to open source. Cohere, we've been investing in it for years now. I'm not so concerned about the number of players. I think there will continue to be good open source options coming from democratic nations like the US, like Canada, like Germany. But I do think that the thing that China is doing that's setting it ahead is effectively distilling the large models. Which is against the terms of service of a lot of these large models. And so that gives them a shortcut. They can very quickly catch up just from distillation of someone else's work. Whereas for the rest of us that are trying to build a completely independent stack from scratch, it requires more effort and more work to build competitive great models. But I do see things converging. I do see capabilities converging between all the major providers.
So what does that tell us about sort of the huge infrastructure spend that we see from the hyperscalers and Meta just talked about this coming off a massive earnings days they're all planning to spend more than $700 billion in infrastructure this year. From where you sit especially as Cohere is building these very specialized models on just a few GPUs does that money come back or do you think that the trend is going towards these smaller more specific models the kind that Cohere is doing?
I definitely think it's pointed towards more efficient models. There's going to be a big wave in the market seeking to reduce costs and make things more efficient. It's easy to do POCs on some of these large models, but then as soon as you push that into production and you're dealing with production level scale, suddenly the price tag just changes the buying equation and cost becomes a huge constraint. So I see things moving heavily in Cohere's direction. We've seen that over the past year with our revenue 6x-ing last year and continuing to grow very rapidly this year. So I do think there's going to need to be a lot more infrastructure because the demand is virtually insatiable. But as that infrastructure comes online, we're also going to make a big push towards efficiency. So there will be much more AI in the world, but those AIs will be using less GPUs to do the same work as they were doing six months ago.
Are the hyperscalers plus OpenAI and Anthropic are they building for that kind of world?
The hyperscalers are definitely building the infrastructure necessary to serve a significant amount of this demand. Although it's important to say that beyond cloud on-prem deployments, private deployments, they're growing massively as well. And this is becoming an increasingly well understood security threat that moving too much to the cloud is a mistake and exposes you to vulnerabilities, especially in this new frontier of potential cyber attacks thanks to these models. And so on-prem deployments, folks building their own data centers, neoclouds, you're going to see a whole new array of compute providers to satisfy all of this demand. So certainly the hyperscalers are building out the infrastructure that is needed. But they won't capture the entirety of the market. They'll probably capture half of it and the rest will be these on-prem secure deployments.
So where does that leave an OpenAI or Anthropic that's not you know the cloud business is not the main business it's frontier models.
I think the demand for frontier models I think the demand for all models is going to be extraordinary. I think there's going to be so it's basically a rising tide lifts all boats. Everything is going to benefit from this. I'm not too worried about the demand for their products or our own. There is a huge market out there. The applications for AI in the enterprise side, it feels like we're just scratching the surface. Like we're still doing very simple things. We know the models are capable. We know the models from 18 months ago are capable of way more than we're doing actually out in the industry. So even if you don't update the models for a year and a half, the industry is still catching up. So right the demand piece I feel extremely confident there's a lot to go do in the global economy.
Good question from Carson Allen, who's a regular viewer of our live streams, asking, is your pricing increasing as you see these compute constraints?
No. So, we don't actually charge our customers for the compute. We deploy inside of their private deployment. So, it's their own compute. What we see with our customers purchasing their own compute, yes, the GPUs are becoming more expensive. There is a scarcity. You know, it's something that we try to help our customers with, but our model of sales is not to provide the compute with the model. Instead, we just provide the software.
