AI's Research Frontier: Memory, World Models, & Planning — With Joelle Pineau

26:56And we also need to have a protocol for these agents to interact with each other and to talk to each other. So I actually think that's a much more likely scenario rather than have like the Uber agent that needs to understand everything and have a fully encapsulated world model. There's a popular thing that AI lab leaders have been saying recently. They've been talking about how there's a capability overhang, how AI technology can do a lot more than it's being used for. Do you believe that? Absolutely. Yeah. Say more about it. Talk about what do you think is not being done that could be done? I see it every day.

27:35I mean, I'll open up a little window. Like one of the reasons I was super excited about joining Cohere is because it's one of the few places that, you know, that we have a team that does research. So I get to see, you know, day to day what's happening in research we have a team that does modeling so I get to see the models that were built and look at the evaluations a full spread of evaluations and we have a product that's product is an agentic platform that is going to real clients so you get to see the whole thing and I see something that our models can do and I see some things that we've built into the products and then we go and there's a lot of customers that are not using the full functionality for all sorts of reasons so I think like that that between like what we have in terms of capacity versus what's being deployed right now.

28:21There's a big gap between that. Sometimes the reasons are capacity questions. Like a lot of actual, we talk a lot about super intelligence, big models. 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 I'm like, well, we could give you so much more. No, it's good enough. And it's a perfectly rational position for them to be taking. So some of that is for efficiency reasons. Some of that gap is also because you're going into organizations which have systems and processes in place.

29:07And sometimes there's like a mismatch between what those processes are set up to do today versus what would be a, I think, you know, a more welcoming environment for an AI agent. So there's these kinds of things. And then the other one is often I think there's a lot of intelligence that is not encoded. So the agents go, they plug into a bunch of internal system, they leverage all the business intelligence with privacy security consideration. They leverage all that information, but sometimes there's big pockets of information that we're not leveraging right now. And if we did, if we connected into that, then we would be able to do a lot more.

29:44So that impedance mismatch in terms of the information sharing from the organization or from the individual to the AI is another case where it leaves a lot of machine intelligence on the table. So we're going to talk about enterprise in a moment, but let me ask you one question about how this applies to consumer. Sure. Obviously, we talked about a lot of technology, and the vision is there within the big tech companies to have a universal assistant, something like an Apple Intelligence or an Alexa Plus. Both of them have rolled out in their own way, but both of them, and I guess Meta has their own product, Google has their own product.

30:25None of these are lighting the world on fire. Do you think that is, is this another example of a capability overing, or is it that the technology is just not there yet? I think both are true. I think, you know, people are expecting, you know, physically been promised super intelligent. So, you know, they are expecting magic out of these AI systems. It is not magic. And so I would say like there's a big gap between expectation of what they can do today. And then there's also a mismatch between, you know, what people try to do versus what might be the strength of these agents. I compared a little bit, you know, you're working in a team, you get a new teammate in like day one.

31:06You may not know exactly what this person is capable of, not capable of. And it takes some time working together. And sometimes that person gets a lot better when you give them a lot more information. And sometimes you discover they have a new skill that they didn't have. But at the end of the day, you know, often that person isn't able to do everything everywhere all at once. And so I think there's both these things are true at the same time. Yeah, there's also, I mean, a lot of corporate politics. I just wrote this story recently in Big Technology talking about how there's like these two basic, and actually you're in a great position to talk about this or give us the real story here.

31:42From my vantage point, there's basically two trajectories that a lot of companies are on. The companies themselves have, I'm not talking about your customers, but if you take about like companies overall, many of them have struggled to put this technology into place. But individuals are starting to see the benefits. So you actually have like these companies with these pilots that are not getting into production. But then you might have somebody, you know, lower down using cloud code who's like actually getting things done. So what do you think about that? And what do you think it means if we end up seeing that divergence continue?

32:18I think that is absolutely true. We see this all the time, even within our own companies. Yes. People's ability to leverage the technology varies a lot. I mean, the reality is we are moving towards a world where there's going to be more and more of that technology. And so the people who have the ability to understand and leverage the technology are going to have an edge. Okay. I agree. All right. Last question before we take a break and go on to some more of the practical applications, some of the more coherent stuff. I still can't wrap my head around the fact that the AI labs are so close together in terms of the technology they produce.

32:57one builds some innovation the next has the innovation one seems like it leaps ahead the next seems like it leaps ahead can you envision a scenario where one of the labs just like kind of hits on something and and can actually open up a lead against the others or is it just going to be neck and neck forever. I think it's really hard to keep ideas in a box, especially because in many ways, these ideas, they reside in people's heads. And I mean, you've seen as much as me, the movement of people between these companies. Like they're always, you know, ping-ponging back and forth. Every five minutes.

33:38They carry the ideas with them, you know, even if the code stays on one side. Like once you've seen some insight, you can't unsee it. Right. And so, you know, they may need to re-implement. They may need to articulate in different ways. They may give it a different name. But ideas just circulate. You can't keep ideas in a box. 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 way to close the ideas.

34:15we should embrace the fact that when you let the ideas circulate, all of us progress faster. And then the question is, let's say all these labs do reach super intelligence, you know, that's been asked, well, you can't hoard it. So where's the economic value in developing it? Yeah, we're still very, very early days in the technology. And we're even earlier days in terms of like, what are going to be the dominant economic models? What is going to be the right business strategy in the age of AI. I think we need to give ourselves the time to experiment. You know, now we have 30 years or so perspective on the internet and the economic impact of that.

34:56And it's going to take a number of years before we figure that out. But often, you know, those who develop the technology are not necessarily the same as those who scale the technology versus those who actually commercialize it versus those who actually control it and regulate it. So So there's a pretty complex ecosystem that is all going to arise out of that. Okay. Well, at the other side of this break, we're going to talk about some real economic impact of this technology already. Talk a little bit about what Cohere is up to. And then we'll cover a lot more. So we'll be back right after this.

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37:23Offer only valid for new Factor customers with code and qualifying auto-renewing subscription purchase. Make healthier eating easy with Factor. And we're back here on Big Technology Podcast with Joel Pino, the Chief AI Officer at Cohere. And of course, this is part of our Davos series that we're hosting at the Qualcomm House here in Davos and running over the weeks following. So Joel, it's great to have you. Let me give you what I've gathered as the use cases in business for AI, and you tell me if I'm missing any, and then maybe what you think is the most valuable. All right, I wrote four down.

38:02One is external chatbots, the customer engagement type of chatbots, the type like Brett Taylor talked about at Sierra. The other is internal knowledge. So let's say a company has knowledge within the company, and it's all fragmented, and maybe there's a bot that you can start to query internal knowledge. Third is papering over systems that don't work. I don't think that needs much more explanation. It's like the story of SaaS. I'm more skeptical about that, but still. And then the fourth is automation. Yeah. Am I missing any big categories as far as AI in business? And where do you think the real value or the biggest category is right now?

38:38I think there's like different ways to slice it. I think that's a perfectly reasonable way to slice it. I think another way that I've seen it sliced is between like predictive AI, generative AI, versus agentic AI, which is like a whole other level of opportunity. And then the other way I've seen it sliced is more by application domains, right? Like, you know, whether it's what AI is going to do in healthcare, what AI is going to do for scientific discovery, what it's going to do in banking, what it's, you know, doing, for example, public sector and so on. So that's the other way that people have looked at the different cases, It's classes of opportunity.

39:18And so what do you think the biggest is?

39:24There's so much potential. I hesitate to pick one. I will say, you know, quite frankly, where Cohere has placed its chips and the core hypothesis is on the case of enterprise AI that needs really high privacy and security guarantees. I think there's a big cluster of applications which falls a little bit in the second category that you outlined, where you have a lot of internal business intelligence information, perhaps fragmented. You want to be able to leverage all that information to empower your employees. And so in that case, especially when that information is something that you don't want to pop up on the web through an API, there's an opportunity to build authentic systems that work in-house with the local data that inform the employees and are essentially like close partners to the employees.

40:17Can you give me like a use case or a case study? Yeah, I mean, we do a lot of work, for example, in financial services, because as you can imagine, a lot of that data is quite sensitive in terms of information. Very concrete use cases we're seeing is for financial analysis. So, you know, we have people whose job it is to advise various clients and they need to pull on diverse set of data. Like what's the, you know, what's the information that's relevant to this particular customer? What's the information that's relevant in terms of like the current landscape, the possibilities and so on? And kind of pull all of that information to make up like a personal plan, a financial plan for a client is the kind of application that this technology can make much easier.

41:02And you can essentially then query your plan, decide, do I have enough information? Do I need to gather more sources of information? And you can combine the internal with the external information, but the output of that stays private, it stays secure, it stays in the hands of just the people who need to see that information. You know, I'm glad you brought that up because I was asked recently by someone in the financial service industry, what's going to happen to entry level employees who were doing a lot of that, you know, collating and pulling in the external information. And I didn't have a great answer.

41:33I, you know, because, you know, you pay, you pay entry level employees less than your standard employees. And you anticipate there's going to be some learning on the job and some productive things. And now the question is, what are these people going to do? If AI can do it for them. If these entry-level employees are able to use AI properly, they're skipping ahead to the level where they can actually be fully functioning analysts and they can essentially do 10x the job with the tools. And so their growth in terms of their ability to deliver value to the employer has just been magnified by giving them the AI tools.

42:08So is then the threat really to the middle? The people who are mid-career who are going to get... I mean, it's like it's the old story of the social media intern who comes in and all of a sudden is managing like PR or marketing for a company. Is it the Gen Z kid who like uses who knows who knows how to prompt and can use Cohere? And all of a sudden the person who's been doing things for 15 years in a certain way has to look over their shoulder. I do think that whenever you introduce a completely disruptive technology, that is a lot of what you see. you see the younger generation for whom that technology is native and is very intuitive and they really, you know, learn how to use it very quickly.

42:52And that just makes them so much more effective and productive. And folks who are not able to engage with a technology as quickly are finding themselves as a disadvantage. I just remember being early in my career, and maybe this is why I didn't last very long in a company and had to go start my own. But, you know, wanting the energy and wanting to do things. If I would have had something that could build a prototype and I could bring that to the meeting and show it as opposed to can I have a couple hours of the developer's time to work on this side project? That would change things. Absolutely.

43:30And to be honest, that capability is afforded to anyone in the company. It's not just the more junior staffers that have access to it. It's also the people who are in leadership position, which instead of writing out a memo, suddenly can go out and produce a full-fledged prototype. They don't need 10 staffers to help them produce their prototype. They have an idea. They can quickly prototype it, and they send that to the team to get moving with a project. So I think that kind of capability is going to open up new ways to set up projects across the organization. This Cloud Code thing has been interesting to watch.

44:10Yeah. It went like overnight from something that will like autocomplete developers code to like will go out on the Internet and do things and build things to accomplish specific tasks. So is this idea of AI systems going out and doing things like on one hand, you know, I hear I see the story of like and I've said this on the show a couple of times. But like, you know, the former Amazon CEO of Worldwide Consumers going out and vibe coding a CRM over the weekend. And, you know, that's cool. But I'm also just like, you know, how real is that? So I'm curious. Oh, okay. You're giving me a look like, yes, it is real.

44:48Well, I think that goes back to my idea, right? Like those who are able to prototype in this way, it doesn't mean that whatever your vibe coded into a weekend suddenly turns into a hundred million dollar business, right? But it's a way to communicate with your teams, your intention. So as long as you have good ideas, you're able to share these ideas in a way that's much more real and to start prototyping much faster. Now, there's other ways to communicate your ideas. There's other ways to direct your teams. But that suddenly opens up so much more. It is interesting how AI is. AI is many things, but it's a communication technology.

45:23It's becoming that, right? And this is the kind of thing that the new coding agents are opening up. Does Cohere have a version of this that it's working on? I would say, yes, we're working on the same kind of capabilities. We're building core generic models. I would say it's a bit of a different experience right now that we're offering in terms of the North platform, but there is a lot of that sort of collaborative work. There's a lot of this going out and essentially deploying agents, leveraging external information. So there's some elements that are similar, but we're less focused specifically on coding use cases right now.

46:06Cohere obviously has raised a lot of money, more than a billion dollars.

46:14But I'll just draw it out. OpenAI sneezes that over a weekend. You have a world now where AI is being developed by a handful of very big companies. your former employer meta is a big player amazon google of course microsoft and then you know open ai and anthropic with these they raise the entire like uh years worth of vc money uh in a round now um what do you think about the risk of the fact that so much of this is being concentrated in so few hands? Honestly, I do think it's beneficial to the ecosystem to have multiple groups who are able to develop models and to deploy them. Just to give you a concrete example, Cohere was very early on working on multilingual models.

47:07So the ability to understand information, digest information across multiple languages, 20, 30, and so on languages. We have a line of AYA models that is really well respected, open sourced and so on. It's just not on the radar of some of these companies that are very focused on, you know, on English centric information. Completely fine, you know, different space for different companies. When we get into markets in Asia, when we get into markets in Europe, suddenly it matters to have a model that is actually state of the art across languages or across the local language. And so that opens up completely new market.

47:46Right now, the opportunities are so broad that actually there's space for, you know, up and coming players to really keep on growing, to have a very healthy revenue, to bring in talent, to actually build new things that are different from some of these other companies are building. So I tend to think it's super healthy to have more rather than fewer companies that are building AI. And I think we're seeing the fact that, you know, going back to my idea of, you know, many different AIs who do many different things, even at a company level, this is what's happening. There's a number of players who are building different things and learning from each other.

48:26But the fact that big tech has so much of it, not a worry? It doesn't worry me. Okay. And I mean, you know, we could have a much longer discussion about it, but it doesn't cause me to lose any sleep over the fact that like what we're building at Cohere has like an amazing, amazing path to be successful. Okay. By the way, I mentioned Anthropic and OpenAI, which have Microsoft and Amazon and Google have massive stakes. And there are many more. Yes. Somebody that does Wario, Dario Amadei from Anthropic. Well, maybe not the fact that he got all those billions from Google and Amazon. But he does have some things to say about the big tech companies.

49:06Here's a thing that he said recently. Some of these companies are essentially led by people who have a scientific background. That's my background. Stemis Asavas's background from Google DeepMind. Some of them are led by the generation of entrepreneurs that did social media. There's a long tradition of scientists thinking about the effects of the technology they built. and not ducking responsibility. I think the motivation of entrepreneurs, particularly the generation of the social media entrepreneurs, are very different. The way they interacted, you could say, manipulated consumers is very different.

49:41So basically, I don't think he wants them running AI. Strong opinions from Dario. Which is, I guess, not something out of character for Dario. Do you think that's a legitimate concern? because it's so interesting you're a research scientist who also worked at a social media company so if anyone knows the answer to this it will be you i mean i i think what what's really important like no one is going to be good at everything right the question is like how do you get others in the room to advise you on how to build something great and you know i spent some time at Meta, I would say there was a very strong channel from researchers to the leadership team, and the opinions were brought into the room.

50:27I think, you know, I've seen that certainly at Cohere, where, you know, the research team, the modeling team, the product team, like there's a room where all these points of views can come together. I go back to this thought, I can't expect one person to have all of that information. And as long as they're building up the teams that are diverse, that are listening to these diverse voices, like they will build better products at the end of the day. Okay. On that note, as ads have started to enter the picture for generative AI, there's a wonder among outsiders like me about whether these companies will do things like engagement max and try to optimize for time spent so they can, you know get those numbers up i don't want to ask you whether you think that's going to happen or not but i want to ask you as a researcher whether that's even economically feasible are the models now efficient enough where like a visit let's say you were to show an ad a visit to just like to serve that visit with an llm uh could be a profitable thing or is it still so expensive to serve these use cases that this even notion of engagement maxing doesn't make sense because economically it's not valid.

51:46I mean, in general, right, like through trial and error, we find economic models that are viable, right? Like that's still how it is. So it depends a lot on the pricing model and so on and so forth. And so expensive ads to buy. But, you know, it depends, you know, it depends on how the model is set up. So I don't know that this is the way that that gets rolled out initially we'll have to see what what's the progression of that i do think you know we have the ability to tailor content based on the information we have that is there that is a lever that's going to continue to be used from an economic point of view ai sovereignty uh before we go uh countries are starting and and institutions like banks are starting to build their own their own models or uh they're not relying on off-the-shelf stuff so talk talk a little bit because this is something Cohere is working on.

52:35It's something I don't know a lot about, the fact that there is this push, or at least it's something that is being discussed. So what is AI sovereignty, and how is it playing out? Yeah, sovereignty has been used in a few different ways. In some cases, it means the ability to have your own model. So in the case of financial services and banks, that is definitely something that they spend a lot of time investing in, thinking about, looking for solutions. They see the opportunity. They were, I think, early adopters even of previous generation AI technologies, predictive models, for example, statistical models, and so on.

53:11And so they see this as the natural evolution. So they're pretty advanced in terms of their sophistication and their readiness for AI. And often they have the means to invest in it. And so we're definitely seeing a lot of interest there. Often though, I think the talent gap makes it a little bit harder for them. So sometimes they've tried to build their own models and so on, and they come to us and they're looking for solutions that are a little bit more mature out of the box and so on. And so we have really solid partnerships going on there. The other way to think about sovereignty that we're hearing a lot is that companies want a robust plan for AI.

53:53And so 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 one model access gets cut off or too expensive, they have another one. And so there's an aspect of sovereignty that's really about building a robust strategy. It's not about just using your own or using one thing, but it's about having control over the access to the technology. Yeah, as you speak about it, to me, it's just amazing how fast this has moved. and going back to our first meeting in 2022, the fact that it's 2026, so it's been three years and change, but it's just a world of a difference year to year to year.

54:33So last question to you, can the pace keep up? It is still moving very fast on so many fronts, just the size of the investments. I think on adoption, we are so early in the curve. And so that's going to be the next challenge to see how do we enable this technology to sort of disperse through society, through the business world in people's lives and how do we do that successfully. But yeah, I think the pace, especially when it comes to commercialization and adoption is really very, very early days. So got a long way to go. Seriously. Well, Joelle, we've spoken a handful of times. I always appreciate how you're able to take these big things that a lot of us are wondering about and grounded in the research and the practical side of things.

55:24So you're always welcome on the show and thank you for coming on. Thank you. Always a pleasure. All right, everybody. Thank you so much for watching and listening. And thank you to Qualcomm for having us here at the space at Davos. And we'll see you next time on Big Technology Podcast. All right. Thank you. That was great. Thank you so much. Thank you. Thanks, everyone. Thank you.

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