Everybody, welcome! Great to see all of you. I have a special treat for you. We have two sessions. We have so many great speakers for you. We broke it up into two sessions. Let's get right to it.
I think we love a world where there's proprietary products, but we also need a world where a whole bunch of companies and different industries in different domains need models as a technology that we could then transform into products. So today, really what I wanted to celebrate is not the proprietary versus open, because I don't think that that's a thing. Proprietary versus open is not a thing. It's proprietary and open. And we love the fact that there are these terrific models that are really products.
Folks think that there are only two types of companies up at the software level of AI. I think that they think that there are foundation model companies that build very large general models, sell access to those models through APIs, do lots of products in different verticals, and then there are application companies that don't really do much AI work themselves, but they build great products on top of the models. And I think that we are really seeing the birth and flourishing of a third type of company that sits in between the two. A company that uses the best the market has to offer at an API level, but then also does great work on the modeling front too, and takes both the best the market has to offer on an API level, their own models, and wraps them all into one, into the best product for a certain vertical.
And in particular, I think we're seeing the rise of a new type of agent happening over the course of the next year or two, where the way you used AI models, it started as you're just calling a model. And then it got a little bit more complicated. It became calling a model, and that model has a bunch of tools that it can use. And I think we're soon going to see agents really be coworkers that can take on tasks that take many hours or many days and do incredibly complex workloads.
We've experimented with things in this domain, in software, where we've experimented with building, for instance, prototype browsers from scratch over the course of many weeks, entirely intent with agents. And when you start to get to these much more complicated workloads, under the hood, you want to be farming out that workload to different models, because different models have different strengths. And they're going to be times when you want to use the computer, use abilities of a foundation model from one of the APIs. And there's going to be other times where you want to use the industry-specific intelligence you have as a company that's focusing on one domain to format your own models. And so I think we're going to see the rise of these compound agents that can be smarter than any one model on their own and mix them all together.
As Jensen said, AI is not the model—it's the system. It's the computer. Perplexity computer is the idea that you should build the orchestration system of everything AI can do. Every single capability—coding, writing, generating multimodal content. So what you want is a multimodal, multimodel and obviously multi-cloud orchestra of every single two-model file system connectors put together. So that all you got to do is delegate your task. You don't have to worry about which model is good at what. It's for the orchestration system to figure it out.
These sub-agents are like musicians, and the models are just instruments, and the work that AI gets done for you is the symphony or the music that they play. And that makes it all pretty simple. And, you know, you get to basically think of any task that AI can do today, any different model. You don't have to feel any vendor lock-ins. And as Jensen was saying at the beginning—It doesn't have to be a dichotomy between open models and closed. We have open models in computer. We have closed models in computer too. And there are different needs for each of them. Open models tend to excel at being very token efficient, cost efficient. Closed models are very good at orchestration, reasoning, two calls, and basically what's happening is models are essentially becoming just tools like file systems and connectors. And we're able to operate at an abstraction about models.
Finally. The first one is that model companies are not actually model companies. Like, they don't just build a model. They build a whole stack, end-to-end. So when you're buying a model from a proprietary or otherwise, you're actually buying the chips, this orchestration software, the inference and product—and all of that has been optimized end-to-end. Now that yields great products. And what openness allows is for other people to basically optimize the whole thing end-to-end. So you're not just buying a model—you're buying a whole system.
I think the other big misconception is on open models, that somehow open models are fundamentally going to be behind the frontier, that, you know, the hit that you take when you adopt an open model is you get control, but you get something that’s a few months behind. I think that's just an artifact of the time where we are today. There's nothing fundamentally different between an open and a closed model. And, you know, models in general in this system, these are it's fundamental knowledge infrastructure. And fundamental knowledge Infrastructure yearns for openness. You know, like an animal, you know, it yearns for the hills or for the forest, like it wants to be open. Like books used to be closed, and the printing press made them open. Science used to be, you know, the real alchemists, and then the scientific journal made it open. And strong encryption, there was a debate between closed and openness. And actually, it ended up being both that started out closed, and then there's a whole flourishing ecosystem of strong encryption that became open. And I think the same thing is about to happen in AI, where there's a flourishing ecosystem of powerful, closed models but equally capable open models that are going to be coming over the next couple of years. I think that's really exciting.
One last thing I did is that, progress is extremely fast. We are on an exponential and everything is very compressed, and there is a lot to learn. There's a lot of study to be done, and it cannot be done completely in the large labs. Because there are tons of smart people out there. But they're lacking access to knowledge, access to tools. And this is where openness can be very helpful. And it doesn't have to be just models—also infrastructure data, general research insights. And this can enable a ton of people out there that can study various aspects of research. And it advances the science of AI, science of intelligence. I see this as a very positive sum.
Pretraining is memorization and generalization and some basic knowledge. That basic knowledge gives you the foundation to go learn skills. If you didn't have that basic knowledge, you wouldn't even be able to teach. I mean, you know, you can't teach someone how to be an engineer if they don't have any basic skills in math and science and, you know, some basic understanding of technology. And so the pretraining part was just the beginning. Most people misunderstand that, in fact, all of your labs do enormous amounts of model development, particularly in post-training and the post-training area. If you were to think about the amount of computing scope in the future, the amount of computing use in pretraining was like 90% of training two, three, or five years ago. But in the future, the amount of training percentage in pretraining is probably going to be tiny. It's going to be mostly post-training.
It is also probably the case that these proprietary models are going to be the best generalists. But it's very unlikely they're the best specialists. And most value is derived from specialists. We need generalist capability all the time. And they're going to get better and better. And as you say, when we integrate them into a system, you get the benefit of an insane generalist as well as an incredible specialist.
The first inflection point that I believe at least I saw that basically made me switch from theoretical physics to AI was a system that my co-founder Ioannis has helped build called AlphaGo, which was the first super-intelligent agent at scale, and it was a 60 million parameter network. It was tiny, relative to what it is now. And it beat the best kind of player in the world at Go. And the thing is, that system never stop learning. It was just an economics problem. How much compute are you willing to put in to get it, you know, to be 10 times better? And at some point, they cut it off because, you know, would you put 10 billion more dollars to make AlphaGo beat Lee Sedol even harder? Probably not.
But you know, now that RL has started working on language models, those are the kinds of questions we'll be asking now, and a year from now, of am I willing to put in $10 billion, $100 billion to solve, to cure a particular disease, right? When you have RL working at scale, the things that you can solve, because these are mechanical brains with endless capacity to learn, become just a matter of economics. And we're seeing the first generation of that in coding, in agentic kind of enterprise applications. But we will be coming to a point where there's kind of fundamental scientific problems, and we're just making economic decisions of whether we want to allocate those resources to have a breakthrough.
And now we're at a point where models are extremely capable. And in order to unlock usefulness, you need to work on this orthogonal capabilities—connecting the context, being able to operate within your data, within, you know, what you're trying to do in your domain. And this is an example of that, having an agent that sort of can operate in your domain within your data, connecting everything together, building this system that's more of an operator. And we have a long way to go to actually make this very reliable and take actions that you want to do with intent that you have. But it sort of like shows that capability and where we need to go, in terms of unlocking more usefulness.
The models and the systems orchestrating the models are going to get much more capable. And so you'll be able to have personal productivity agents that can take on more complex tasks that run for longer. And I think that also these compound agents they will get much better at using tools.
