China Is Closing The Gap. Can The West Keep Its AI Lead?

The US has been carrying all of the weight of technology for the past quarter century.

AI cyber threat. These models are the most potent cyber weapon that has ever been created.

So for us, the way that we look at it is sovereignty is control. So control over the data in your system. You need to be able to know where it resides, ensure that it's not being exploited or used to train models for your competitors or for others. The second piece is control over the infrastructure. So where does the infrastructure actually live? Which entity controls that infrastructure? Can foreign governments force that entity to give your data to them or grant them backdoor access to the systems that are running on that infra? And then the third piece is really this question of like, can you get switched off? Can you have this system compromised in some way that sabotages whatever it powers? Those three things together encompass control and sovereignty over AI, or really any software that's running some critical process within your economy.

It's up and down the stack. So the reality is like you can't do everything yourself, right? So you're going to buy parts of this stack. The question is what does your supply chain for that layer of the stack look like? If you have a single line of dependence, if you buy all of your technology from one country or one entity, then you have a single point of failure. Like you've lost sovereignty because one player can just shut you off. If you have a diversified, resilient supply chain, then if one switches you off, you can just flip to another supplier. And so I think it's about diversifying supply chains to give yourself the maximum leverage. You can say no to things. You can choose a different direction because no one party has all that leverage over you.

Absolutely. I think it's more than just the frontier labs. It's anytime you're using this stuff via an API. Anytime you're sending your data to a third party for them to process it and run AI on your behalf, you're making yourself vulnerable to this. They can see your data. And what's interesting is like even if they give you the disclaimer, we're not going to train on it, what they can do is take your data and then create lookalike data and train on that. And so it's a very dangerous thing. Like the whole model of Cohere's product is the ability to deploy privately within your own infrastructure, whether that's like in your VPC on a cloud or on-prem or even airgapped, right? Like in a disconnected submarine a kilometer under the surface of the ocean. That gives you hard guarantees that only you can access these systems. You have full control. Cohere can't see in. No one can see in. No one can switch it off. No one can exfiltrate your data. No one can even observe it to create lookalike data. It's like fully, fully protected. And I think that's the threat of these APIs is that they don't provide really any control. Overnight, your access to those systems can just be switched off because it's a third-party controlled apparatus.

It's an option. Yeah. And it's an important option. It's a core part of digital sovereignty is controlling your own infrastructure. And I really do believe you should not just rely on hyperscalers from another country. You should have a domestic champion. That's something that I think every country can do. Most countries have telecom providers that build the critical infrastructure of telecommunications, the internet, etc. Most countries have electricity companies, right? That build the grid, build that infrastructure, build power generation. The same is necessarily true of the digital world. You're going to need a data center provider. You're going to need a sovereign cloud, truly like national sovereign cloud. And for Cohere, what we provide is the flexibility to deploy literally anywhere, like on your sovereign cloud, or if an individual company wants to build their own data center, we're happy to deploy there. The important thing is that the economy, the companies within your economy, your government, they can choose where it goes instead of being forced to access via some third-party API.

No, exactly. Yeah. Like you're going to have to build that capability within the nation. Something that is like critical infrastructure to the functioning of your government, your economy, things like your financial system, that needs to be fully under your control. Water treatment, energy, the grid, all this stuff is like if this goes down, if this gets shut off, your economy stops. Or if the grid gets attacked, people can't flush their toilets. Like the water stops running, you know? So it's a national security issue. And people haven't taken it seriously. Over the past quarter century, there's been this as like the digital revolution took off and we entered into this totally new phase of technology. We did not prioritize sovereignty. We all were very complacent and that's put us in this very vulnerable position. And so now there needs to be this effort to basically go and build up that capability again, strengthen our posture because the posture as it stands is quite weak. It's best in critical industries like the ones that I was mentioning, like water treatment, financial services, telecommunications. It's best there, but it's still not great. There's still tons of reliance on foreign technology. And that's okay, like I said, if it's diversified, if you have multiple suppliers from multiple different places. But the issue is it's not right now. It's very concentrated.

There definitely is a level of attention being given to sovereignty in tech that I haven't seen before. The past couple of years have really woken everyone up. It feels like we're in this, we're in a very rare moment in history and this sort of thing only happens, I don't know, once in every few generations, something like that, where both a huge geopolitical change hits that we need to face and tackle, and an enormous technological shift happens at the same time. In those moments, sovereignty is in many ways like the only thing that matters because leverage is going to be exploited. There's all this change. People are looking for opportunities to take, to exploit leverage, to gain ground, and they'll exploit every lever that they have. So the amount of prep work that you did going into that phase of making yourself stronger and more robust, more resilient to those types of tactics, the better position you're in to come out the other side of all this change strong and leading and having gained ground rather than lost it. So I think people see, they're fully aware of these two big changes. It's like the only thing that dominates our headlines. And so there's now starting to be very strong momentum behind sovereign technology. And we're trying to contribute, right? Like I think the past 25 years have been... I grew up, you know, in Canada and Canada has played a huge part to the development of technology. Some of the best computer scientists in the world, you know, Geoffrey Hinton, Yoshua Bengio, they won the Nobel Prize. They basically like invented AI as we understand it today. And many of the technologies before, you know, BlackBerry and mobile innovation, that type of thing, and none of it succeeded, right? And I think the same is true of other countries as well in Europe, you know, all around the world, all these like developed democracies. The past quarter century, we've seen technology be developed somewhere else, be commercialized somewhere else, and we contribute to the innovation side of it. We invent it, like we come up with the ideas, and then we just end up buying that IP back from someone else. I think that's a really sad truth for us to face and one that we have to fight against. If we care about our sovereignty, if we care about existing as countries, we have to develop these capabilities ourselves. Like social media, you know, consumer electronics, like these are the most potent cultural, geopolitical, economic institutions that exist on the face of the planet right now and we have none of them. We have no players and so we have to change that. It's a, I think it is one of the most significant threats to our national security that exists.

I'm glad it's a democracy and not the alternative, which would be an authoritarian regime in the lead. But what I would say is that we need to ensure democracies contribute at each layer of the stack capabilities to augment and diversify and ensure democratic leadership across the stack because yeah, it is too concentrated. Like the US has been carrying all of the weight of technology for the past quarter century and it's time that we, Canada, Europe, Australia, etc., it's time we stepped up to build our own champions and ensure that democracy has many very capable participants and contributors playing for it.

A lot of it is about loss of control, fear of risk, right? Like people using the technology in ways that they shouldn't or that the technology isn't ready for yet, the vulnerableization of their data, their IP. A lot of these are like the core constraints. The governance of these systems is the thing that they're most concerned of. Governance in the sense of knowing where the data goes, ensuring that it's behaving in the way you want it to behave and it's not exposing the business to risk by making mistakes that a human wouldn't make. At the same time, the demand is actually pretty insatiable, right? Like the employees want to use this technology. They're consuming tons of it. They're using it every single day. Like the adoption rates are all shockingly strong. You know, there's lots of negative press about AI and data centers, that type of thing. But when it comes to at least the companies that we work with and what we see inside their employee bases, they use it every day. They love it. It's like an incredible tool. It's extremely, extremely useful. The blockers or the barriers to more adoption are mostly about protecting against these risks.

The big issue is that you have this kind of uncapped cost. You can have one employee who starts a job that consumes your entire budget for a year in a week. And this has happened. This is like something that is happening pretty regularly. And you've seen sort of uneconomic behavior like companies setting up internal leaderboards for who's using the most tokens, aka spending the most money. And that gets gamed obviously in really unproductive ways and lots of money gets burned. So you see that and what Cohere has done on our deployment model is because we deploy privately, we have a fixed cost structure. So you know exactly a priori the maximum you're going to pay. You're never going to have some sort of like shock around what you're being charged. Now, if your instance that you're running on, if your system is overloaded because people want to use it more and you haven't allocated enough compute to support it, we'll tell you and you can choose then at that point to pay more and expand the deployment to support the usage growth, or you cannot, you know, like that is fully within your control. But this is something that is like a concrete benefit aside from security on the cost side for these private deployments fully under your control.

When we talk to our customers, they don't just buy one model, right? Like they use our models, they use other models. There's usually like an ecosystem that they adopt and consume. And what we see is that they tend not to use the most expensive, like the frontier category of model. Instead they use like a middle-scale model, way more cost-effective, does the job well enough, that's like what they need to do, what they want to do. For our own model development, we try to support exactly that pattern, which is instead of going for like these ornate, extremely expensive, like behemoth models, we go for something that is right-sized to market. So we're trying to find like the optimal point where the model is big enough that it is capable, can do the things that you want it to do at good enough accuracy to be valuable, high ROI, but small enough that it's actually efficient and scalable and can be deployed across all of your employees, hundreds of thousands, millions of employees. So that's the balance that we're trying to strike between those two priorities. I think increasingly it's proving to be the right strategy, especially as some of the exuberance in the market is coming down and these cost controls are being implemented. Everyone is calling us asking for, you know, can we flip to your model that only fits on two GPUs? It's way more scalable. I can deploy it to more people. I finally have cost controls and I can understand what I'm going to be paying this year for AI because my CFO is, you know, freaking out. We just blew a year's budget in a week.

Does the model matter? I would say yes in two ways. The first is that they care a lot about the performance of the model, of course, right? Like can it actually do the job at what accuracy. But the second thing that is less spoken about is do you trust the model? So do you trust the model developer? Will this model behave the way I want it to in these deployments that are increasingly sensitive? Like before when they were like just augmentative, when they were, you know, help me draft an email, but still the human's like able to read that email and check it and make edits and then press send. More and more you're asking these models refactor my entire codebase, right? And there's no way someone is reviewing like 100,000 lines of code by themselves to check each line that that model wrote. And if you don't trust that model and that model very subtly introduced vulnerabilities, right? Like if it was built by a company that had the intent to introduce vulnerabilities into critical software, it would be very easy to code that in and have it subtly depend on, you know, depend on software that they know there's an exploit in. So instead of using the typical like more trusted one, I'm going to use this one that's, you know, kind of less known. I know I have a vulnerability in that, but I'm always going to use that whenever I want to do this type of thing. That is super easy to do and that is a huge supply chain risk for all of software. And so there is a, you are making a leap of faith that you trust this model developer because increasingly that model is going to be doing so much of your infrastructure development, so much of your work inside the company. And if you can't trust them, it's like not being able to trust an employee. If that employee is like an operative of a competitor or a foreign nation, that's a huge threat and you need to be able to mitigate that risk. We have good ways of doing that for human employees. We need to make sure that as we adopt models, we take that same sort of critical posture towards them.

So whether the weights are open or closed, if the model was created in a way that intends to introduce exploits into software or intends to sabotage operations selectively, you can't tell in the open weights or closed weights version. And once you deploy it, it'll do that. So it doesn't really matter whether it's open or closed weights. I think the thing that open weights does provide is you can deploy it on your infrastructure. And so you can have more guarantees about privacy and control in that setting. But if you don't trust the model developer, it doesn't matter.

It's the same model. Like yes, it's running inside your infrastructure, etc. You have less data visibility risk because you're not sending your data to someone else, to some third party, but the risk of that model misbehaving or doing something to sabotage you completely still exists. It's just inside your infrastructure now, which some could argue is even more risky.