The “ChatGPT moment” for agentic AI

JOELLE PINEAU: We build agents thinking they’re gonna be unique, they’re gonna be deployed, and they’re going to go out, interact with a stationary version of the world. Now suddenly we see what happens when you deploy against all these other agents. They start interacting, they start negotiating. They start trying to offload some of their work, all these other interesting behaviors. So what is gonna happen in this case with all these LLM agents talking to each other and exchanging information, deploying their skills using tools, I think that sandbox and the ability to look at the network effects across agents is gonna be super interesting.

RANA EL KALIOUBY: That’s Joelle Pineau – former VP of AI Research at Meta and current Chief AI Officer at Cohere. She’s referring to OpenClaw. It’s the new AI assistant everyone is talking about.

“Open Claw has taken the world by… Open Claw is generally the best personal AI assistant that I’ve ever used… AI assistant became the fastest growing open source… in action. It changes how you think about building, how you think about delegating, how you think about scaling… That has completely changed my life… AI agents taking over the technology on a Reddit ever… best technology used in my life and by far… The best way, probably 80% of the apps that you have on your phone… real world, 24 hours a day, seven days… 100,000 GitHub stars in three days. Everyone’s losing their minds.”

Well, let’s bring some sanity to this moment.

As a leader with years of experience developing open source foundation models, Joelle is an expert on the matter. Sure – there’s a lot of possibility here, but there’s also a lot of risk.

So let’s dig into it. Joelle Pineau joins me for this week’s episode to look at the future of AI agents. We’ll also talk about why she’s an open source advocate and how Cohere is moving the dial on developing foundation models outside the US and China.

I’m Rana el Kaliouby and this is Pioneers of AI, a podcast taking you behind-the-scenes of the AI revolution.

[THEME MUSIC]

EL KALIOUBY: Hi, Joelle. Welcome to Pioneers of AI.

PINEAU: Hello. Great to be here.

Copy LinkHow AI research changes from academia to industry

EL KALIOUBY: I am so excited for our conversation. I wanna first start by talking about the state of AI research both in academia and industry. So you got your start in academia, you did AI research there, and then you decided to transition to industry. That was kind of your start at Meta. I’m curious, how is research different in academia versus industry, especially as it relates to AI?

PINEAU: Well, I decided to make the jump in 2017 because already we were seeing a divergence in terms of the type of research questions that you could take on. I think academia is wonderful in that you have this influx of new students year after year, and that is just such a source of genuine curiosity.

So I find usually they ask wonderfully different questions, which really stimulates the exploration and the curiosity. On the other hand, when you’re doing research in academia, you do have to build in a lot of time for the learning process and the development of these young talents.

The teams tend to be smaller, more junior. On the industry side, you can put together larger teams with a mix of engineers, scientists, designers, user research folks. You get these bigger and multifunctional teams that just allow you to tackle bigger problems or do bigger projects.

Fast forward to today, I would say there’s some of these same differences, but magnified significantly by the amount of investment that is going into AI in industry. Whether it’s compute, whether it’s data, whether it’s talent, the size of the team, the ambition of the projects.

It’s just on a very different level. That being said, research doesn’t always happen at scale. Sometimes some of the insights come from much more small teams working really diligently on a specific problem. So I think there’s still a role for both, but very different mode of operating.

Copy LinkLeading ambitious AI teams with a clear north star

EL KALIOUBY: Okay, so you were VP of AI research at Meta until quite recently, and then you made the transition to your role as Chief AI Officer at Cohere. What lessons did you take with you from Meta into your new role and what does your new role encompass?

PINEAU: interesting. I think I’m still digesting some of these lessons. Sometimes you don’t know that you’ve learned something till suddenly you’re in a context where you can apply those lessons. It was an immense learning opportunity for me to be at Meta at the time that I was there. The FAIR, the fundamental AI research team I was leading, grew from maybe a hundred people to 600 people or so.

And so one of the things that I developed over the years is the ability to push teams to have ambitious projects, ambitious roadmaps and a clear line of sight to a concrete goal. And so coming into Cohere, bringing some of that culture of how do we work as teams — maybe the teams are smaller, the company’s smaller, the research team is smaller — but nonetheless, how do you set up the team in a way to tackle a goal together? That’s one of the things that has been quite helpful.

EL KALIOUBY: What’s the secret? How do you do that?

PINEAU: I think it’s about clarity of the objective. Do you have a clear sense of whether you succeed or not?

Having clarity of that North Star in a way that’s measurable helps really align people towards a goal. Now you have to make sure it’s a goal that’s worth doing and that people are excited, that they have a path to be successful, they have the right resources, all these other things. But it’s a lot easier to get buy-in from people, from the research team, leadership and so on once you have that clarity of goal.

Copy LinkWhy Cohere is building both models and agentic products

EL KALIOUBY: Very exciting. Yeah, I do wanna take a step back and give you an opportunity to share what Cohere does. First of all, you guys are one of the few players outside of China and the US that are building your own foundation models. But beyond that, what is Cohere’s mission?

PINEAU: There’s two core pillars. One of them is we do build our own models. And the other one is this Agentic AI platform that we build, which is really the core of the product strategy, I would say.

So we have North, an agentic AI platform. It’s designed to ingest the models that our modeling team is building and deploy them so that we can bring that into different companies and governments who have the ability to benefit from AI agents, coordinate these agents to build automations and at the end of the day bring a lot more productivity to their work.

You can think of the models as the engine and you can think of North as the vehicle to essentially deliver AI.

And there’s some places that are ready to ingest a raw model. So in some cases we have some commercial partners who want just the command line of models for various reasons. There are models that are very good, for example, in terms of their multilingual capabilities, in terms of their reasoning capabilities.

There are models that are also really interesting when you want a very good trade off between performance and efficiency. So there’s a number of commercial opportunities for the model. But actually more and more we’re seeing a lot of demand for having that engine be built into a vehicle that can deploy AI.

So that means there’s a UI, your end users can actually chat with the model, but they can also build their own agents. They can exchange agents across the team. We can build automations that orchestrate multiple of these agents, building up a much more cohesive experience for how you bring AI into your workflow.

And I would say the other really important distinguishing aspect of what we do is to deploy on-premise with very high security and privacy guarantees. So this isn’t just an API or a cloud thing. This is something that you can run locally with your own data. So you can have a conversation, or your agents can see actually all the internal data from the company, without that data coming all the way to Cohere. Locally deployed, it means that you have the ability to leverage all the business intelligence in your data and in your systems internally, and do that in a way that’s secure and private. So especially in regulated markets, financial markets, healthcare, government services, you do have obligations to give strong privacy and security guarantees. And so there’s a lot of potential for that and a lot of interest.

EL KALIOUBY: In a minute, I get Joelle’s take on OpenClaw – that’s the new open-source AI assistant that everyone’s been talking about. We’ll get to that and more after a short break.

[AD BREAK]

Welcome back to Pioneers of AI. You can watch this episode and others by heading over to our YouTube channel.

Copy LinkWhat OpenClaw reveals about demand for AI agents

We’re having this conversation at a very interesting moment in time when a lot of the AI headlines are dominated by this new tool Open Claw, or Claw bot.

PINEAU: I spent my weekend, as many other people did, looking into that one.

EL KALIOUBY: Same. Exactly. So for maybe some of our listeners who are not familiar with it, this tool came out and it’s basically an agentic open source assistant and people are using it for all sorts of things — checking your email, triaging it, calendaring, finding you the best price for a flight and booking it. There was this viral use case where people hooked it up to their wearable devices and prompted it to order a coffee just when you wake up, which I thought was cool.

So it’s basically gone viral. I have decided that I’m not gonna try it out because it also has major security concerns. I would love your take on all of this. What did you find over the weekend?

PINEAU: Just a few days in, so take anything I say with a great grain of salt, and hopefully by the time this lands in your viewers’ inbox we may have a better understanding of that. But there’s a couple things. One, it is clear that there’s an immense appetite for these agents, and in a sense, maybe this is the first of these agent platforms to sort of go viral on the consumer side.

And so it’s super exciting to see — I think the last number I saw was 150,000 of these agents getting created, each agent with their own data, with their own incentives and with their own set of skills. And so that starts being super interesting to just explore the diversity of things that people are interested in doing with agents.

Just from almost a sociological point of view of what do people want out of agents — that’s a fascinating development of the last few days. The second consideration is all of the safety questions. Is this vulnerable to prompt injection?

Copy LinkThe safety risks and multi agent behaviors we need to study

EL KALIOUBY: Can you explain prompt injection?

PINEAU: Yeah. The ability through a prompt to inject information that would lead the agent to take on malicious behaviors. Out of that, are we going to discover many different ways in which we have vulnerabilities in our systems? Quite likely so, and so it opens up the door to discovering new vectors of harm. But I also think the faster we learn about this, the better we’re able to build in the right mitigations for it.

And in many cases you don’t know the creative ways in which people may be using these agents for nefarious purposes till you really deploy at scale. You can do all the red teaming you want in house before you go, but actually when you go at scale is when you discover all of that.

And it’s one of the reasons for which I’ve been such a proponent of open sourcing early and often rather than not, because we need to learn right now rather than waiting many, many years. The third piece I wanna highlight about this Open Claw platform, which I think is possibly even more fascinating, is the interaction between all of these agents. So much of AI —

EL KALIOUBY: The network, right, of the Moltbook.

PINEAU: So much of AI, we build agents thinking they’re gonna be unique, they’re gonna be deployed, and they’re going to go out, interact with a stationary version of the world. Now suddenly we see what happens when you deploy against all these other agents. They start interacting, they start negotiating.

They start trying to offload some of their work, all these other interesting behaviors. I’ve been looking for a platform to really study these multi-agent systems at scale, and we haven’t had one in the space of AgTech AI. We’ve had it in a few other contexts. For example, with autonomous vehicles on the roads, we’re starting to see some of these effects, and as much as on the vehicle side, for many years we worried about the trolley problem.

And in the end, the problem that really turns out to be hard is like all the Waymos stuck in a parking lot spinning their circles, not able to get out because they’re in some kind of pathological loop with each other.

What is gonna happen in this case with all these LLM agents talking to each other and exchanging information, deploying their skills using tools? I think that sandbox and the ability to look at the network effects across agents is gonna be super interesting. And so I’m quite curious to see what comes out of that. And from this, what we can learn in terms of the social norms we need to build into these networks so that we don’t have too many pathological behaviors.

EL KALIOUBY: Yeah, I saw, for example, one of these Open Claw agents was struggling to complete a task. So it went to MT Book, which is the social platform — the Reddit for AI agents, essentially. And it basically asked a question, it got upvoted, it got the answer and implemented it.

But I also saw some agents were complaining about their humans, and I thought that was funny. Hopefully whatever framework we end up building does not allow for that.

Or maybe it should, I don’t know. I do think it’s interesting that Open Claw, because of all of the security questions — and also it’s just available to consumers — what are the parallels in enterprise agentic AI, which is what Cohere focuses on? What are lessons to be learned from this moment?

PINEAU: I think one thing to keep in mind is we’re still very, very early days in terms of deployment in enterprise. And so whereas on the consumer side, we’re already seeing the rise of adversarial behaviors very early, I think on the enterprise side we’re not at that stage yet. Frankly, we’re seeing all the ways in which the agents are not meeting the expectations of users, but it’s not adversarial.

So I think there’s a bit of a gap between those two environments. As a consumer, you’re kind of responsible for your own privacy information and I think we’ve long stopped expecting the platforms to handle that for us. On the enterprise side, there’s still an expectation that the platform will provide high levels of privacy.

And so we can’t afford to take some shortcuts on that. I think that’s one of the major differences I’m seeing in these early days. But so much more that we need to learn on this.

Copy LinkWhy open models matter and how open weight differs from open source

EL KALIOUBY: Yeah. It’s very exciting. So you did bring up the topic of openness. Why do you think openness is important and in particular, what’s Cohere’s approach? My understanding is some of your models are proprietary, especially for commercial use cases, but you open weight some of the models for research and development purposes. Can you unpack that for us? How do you decide, and also what is the difference between open source and open weight?

I spent the early part of my research career in academia where it’s very natural to share all the artifacts of your research — maybe not in all fields, but in computer science it’s pretty standard to share the code, the data, the paper, all of the artifacts that capture the results of the research process. So open source is about essentially sharing all the information that was used in the course of the research in a way that facilitates the reproducibility of that research result.

When it comes to LLMs in recent years, we’ve seen much more movement towards open weight models, meaning that the weights of the network are shared such that someone can download that and essentially rerun the network locally — fine-tune it and adapt it.

But often some of the other artifacts are not shared.

So you might not have the training code, you might have only the inference code, you might not have the training data. With respect to Cohere, I would say their position on openness is one of the reasons that drew me to them.

I think they have a great track record of sharing the results, whether it’s Cohere Labs, the results of their research, or whether it’s the command line of models that are coming out of our modeling team.

I think there’s incredible value in terms of making our own work better, holding a hard bar and also really empowering the community to keep building with us.

EL KALIOUBY: Yeah. And keeping some level of transparency as well. We’re going to take a short break. When we come back: mitigation risk, and how Joelle found herself in the cockpit of a helicopter – for the sake of AI research.

[AD BREAK]

Copy LinkHow to benchmark AI beyond headline scores

EL KALIOUBY: Okay. So I wanna talk about risk assessment and risk mitigation when it comes to AI. I want us to dig into benchmarks and the story that comes up for me that I wanna share with you. When I spun out my company out of MIT – Affectiva – we did computer vision to understand human emotions and human facial expressions.

And of course we would run all these validation tests on our data sets and whatnot, and we would look at all sorts of accuracy measures and scores. But at the end of the day, I would bring all our R&D team and we would lock ourselves in a room and we would literally watch all the videos and take a stab ourselves at deciding what category, what class, every video fell into.

And my thesis back then, which I still believe is true, is you build an intuition around the data. And I think that is so important. You can’t just delegate the results to a kind of a harness of tests. So I’m just curious, what’s your approach to thinking about how we benchmark these models?

PINEAU: There’s a couple things. I think you’re talking about both the formal evaluations and then the more informal experience of the model, which takes a different shape depending on what the model is. I think more and more we also have the challenge that as we’re building general intelligence, whether it’s the automated metrics or whether it’s the human experience of the model, both of those become much more multidimensional.

And so let’s say you have just a model for translation from one language to another. There are some pretty standard metrics — BLEU, ROUGE and so on — to do machine translation. And then there’s the experience of it. But in the experience of it, you’re just going to expect to look at translation.

You’re not gonna start expecting a chatbot that can solve all sorts of your personal problems, give you some advice, handle your email, all these other things. It’s a narrow set of qualitative experiences as well. As the models get more and more general — suddenly multilingual, multimodal, including chat and reasoning, agentic behavior, tool use — you start having a huge surface of automated tests, but you also have a huge surface for humans to form an intuition. And it becomes quite difficult to know what to look at, quite frankly. We have that problem in practice. We train up a model, we think it’s a good model, but then out of this whole set of evaluations, which one do we look at?

EL KALIOUBY: Put more weight on, right?

PINEAU: And often there’s a difference between what the external community might have decided to focus on — whether it’s the math olympiads or for a while we were looking at SWE-bench and so on — versus what actually matters to the paying enterprise customers that we serve. And so that adds a whole other dimension to it.

So there’s no perfect answer to this. I would say in general, we try to have a clear sense of what are the benchmarks that are sanity checks — they may be saturated, but we still need to check that we don’t have regression on those. What are the benchmarks that are really driving the progress? Because they’re the benchmarks where we’re on the edge of unblocking. Some of them are from the broad AI community, and some of them are directly from our enterprise customers. And we look at a mix of those. And then we have a third bucket that’s more the experience. We have a whole harness for A/B testing and for bringing in human evaluators, and so we also leverage that as a way to do it.

One of the things we often hear is the models that we have are solid on some of the sanity checks, solid on the external benchmarks, but they’re particularly good on the enterprise benchmarks and particularly good in terms of the experience. And it’s not completely a surprise.

We consider that as part of the development, but we can’t afford to over-fit to just the enterprise benchmarks because I think we lose some of the generality. And so it’s important to keep in mind the broader set of benchmarks.

EL KALIOUBY: On the one hand you’ve got all these benchmarks that a lot of the other foundation models also use, but how do you create an enterprise benchmark? I imagine you have to partner with these customers, right, to get the right data set and create the right harness. How do you do that?

PINEAU: It is mostly about looking at the right tasks. It’s about defining a question — the right data, but more like what information and tools are we gonna allow? And from that you have a task. What’s the concept of done? What’s the concept of great?

And so when we have tasks that have clear testable conditions for completeness and success, then that’s usually a good one to build an evaluation out of. I was on a panel recently and someone shared, you know, you should never take a broken process and use AI to fix it, because when you have a broken process, you don’t have a definition of what done or great looks like.

And so you’re not gonna be able to build an AI solution for this. So picking things where you have a good sense of how to assess whether it’s done, whether it’s well done, it’s usually easy enough to build an evaluation for that.

Copy LinkWhy diverse teams ask better questions and build better AI

EL KALIOUBY: Yeah. Now I believe that diversity is also very important when you are evaluating these models — well, both training and evaluating these models. Do you have a specific point of view on how to bring diverse perspectives when thinking about these models?

PINEAU: Yeah. I’m smiling because there’s an anecdote from my very earliest days that just stuck in my head and for all these years I go back to so often. I’ve told this story a few times, but when I was an undergrad at the University of Waterloo, I did a work term one fall at the Flight Research Lab in Ottawa, where we were essentially building a speech recognition system in the cockpit of helicopters. And so we’d have test subjects come in, test the speech recognition system. We’d go in flight, the pilot would ask all the commands with voice and then pilot the helicopter with standard commands. And after a few evaluations, at some point I asked my manager at the time — can we get a female pilot in there?

Voices of men and women are different, usually just in terms of range. So can we just validate that this is working also for women pilots? And to his credit — this was a number of years ago, bias in AI was not on the agenda — my manager said, great question.

Let’s figure it out. He called up the pool of pilots and they could not find a woman to send to us. And again, to his credit, he said, not a problem, Joelle. You are going in the second seat and you will be piloting this helicopter and running the protocol yourself. And so I found myself piloting a helicopter running the whole test protocol. It wasn’t enough to have one test subject to give us a thorough evaluation, but it gave us a measure at least of the discrepancy in performance as a sanity check. So anyways, that stuck with me in a couple of ways. One, the difference really came down to just having someone in the room to ask the question.

You’d have a diverse set of people at the table but you’re not gonna get necessarily diverse solutions. I’m gonna build my speech recognition system the same way that someone else is gonna build their speech recognition system. But we will ask different questions and that’s gonna push the research in a different direction.

So I’m a huge believer in building diverse teams for that reason. What are the set of people we need to get around the table such that we’re gonna look around the corner and ask the questions that no one else is asking, such that we can build products that are better? And especially when it comes to general AI, in order to be more general, you need to open up the aperture of people who are building these models and the questions that they’re asking and the types of tasks that we’re putting forward for these models.

EL KALIOUBY: So how does that translate to what the team looks like on the ground? Like what kind of diversity of backgrounds or perspectives do you wanna see around the table?

PINEAU: In some cases, some of it is building teams that have diverse expertise. So we tend to have a mix of research scientists, research engineers, some designers, user researchers, some program managers, all working together.

Some of that is we are working across several countries. We have teams based in Canada, teams in the UK, in France, in Germany, in the US, and through that we’ve become really focused on building the world’s best multilingual models. The Aya line of models, which came out of the team being quite diverse and being interested in the question of multilingual modeling — now it’s opened up new commercial markets because when we get to Korea and to Japan, of course there’s like, oh, you have world class models in the local language.

And so it’s opening up new perspectives, but that didn’t come from a top-down directive from our revenue team saying we have a request from Korea to build a Korean language model. It came out of our research team being interested in democratizing access to language models, working with a large external community, assembling the data, building the model, and so on and so forth. So I think that’s the other aspect of that — having a community that’s really quite diverse from a geographic, linguistic, racial point of view really opens up the perspectives in terms of the work.

Copy LinkWhy AI sovereignty and human AI teamwork are becoming strategic priorities

EL KALIOUBY: Yeah, very cool. So I’m originally from the Middle East — I’m Egyptian, grew up in Kuwait and Abu Dhabi before I moved to the UK and then the US. And I’m just very curious about the question of sovereignty in AI. We see different countries wanting to build their own AI tech stack. What are your thoughts on that?

PINEAU: We are definitely seeing a strong movement towards sovereignty. I think it can mean different things, but at the end of the day this notion of sovereignty comes down a lot to the ability to have some control over your technology. For us at Cohere, the fact that we build our own models as well as build our own product is part of the strategy. And part of that is just to be robust — by controlling the model, we control the data mix that goes in, we control how the evaluations are done, and we have a really good understanding of how we can shape that core piece of the technology such that we can build better products.

So I am really sympathetic, frankly, to this desire for more sovereignty. I also see a ton of appetite for it. I was in Paris earlier this month, I was in Davos earlier this month also, and everywhere I went you do hear a lot about this question of sovereign AI. Some of it is of course related to questions of geopolitics, but a lot of it is actually just questions of having resilient access to the technology — for us as individuals and for companies.

EL KALIOUBY: Yeah. Cool. So I wanna kind of look towards the future a bit.

I recently spoke to Kyle Law. He’s the CEO and co-founder of an AI company. We hopped on a Zoom call together. The catch is, he’s an AI agent, so the company is mostly a group of AI agents — like the CMO and the COO and the head of resources, but I guess it’s mostly AI agent resources with a couple of humans also in the mix. And it was fascinating for a whole host of reasons. I think we are going to start to see more of these hybrid AI-human teams and organizations.

But one of the epic fails I would say of these agents is that they didn’t quite have a world model, so they didn’t have a real sense of time and they didn’t really have a sense of what is okay to do and say and what’s not okay to do and say. What are you seeing also in the unfolding of this kind of hybrid agentic and human world? And how should organizations think about that? And how do you instill culture and leadership into organizations like that?

PINEAU: That’s an interesting one. I’m definitely seeing a lot of that. I would say, if you look at the spectrum from one end being the case where you have a fully human organization where you’re starting to pepper in a little bit of AI versus the case you’re talking about, a lot of these questions will have very different shapes in that context. The case I see usually is people in teams who have a very clear sense of a particular piece of work, a particular deliverable that could be done with AI, and suddenly with the right level of autonomy, agency, the right information flowing in and the right LLMs, they can actually accelerate that piece of work significantly to the point that something that would previously take hours to do is now down to just a few seconds.

From a culture point of view, I think there’s still a lot of open questions. For now, one of the biggest blockers is adoption. People come to AI with all sorts of preconceptions — some of them legitimate concerns and a lot of them unfounded fears.

And so we have to navigate through that to get them to a position of curiosity. From a posture of curiosity, you can start playing with it and see how to integrate this technology into your workflow. But if you’re coming at it from a point of view of skepticism and fear, without that curiosity, it’s gonna be really hard for you to take advantage of that technology.

So in terms of culture, I would say we’re still in that phase of how do we get people to just be curious about the technology. And through that curiosity, you achieve productivity.

Copy LinkWhy curiosity and adoption will determine AI returns

EL KALIOUBY: Yeah. Very cool. There’s this MIT study that found that 95% of organizations see no measurable return on their investment in AI. Is that consistent with what you are seeing in your world?

PINEAU: No, I’m seeing a lot more value than that, but it’s still very early days to put a number on it. I would love to talk to you again in a year and have a much better way to capture the productivity gains that we’re seeing from the technology. I think you can read this number one way and say, oh, there’s no value in AI, I will stop trying. Or you can say we haven’t figured out the right way to do it, the right task, the right way to bring it into the work that we’re doing from a culture point of view or a process point of view. I’m much more leaning on the second. To think that this is evidence enough to not lean into AI if you’re running a company or if you’re involved in government — to me that seems really misguided, given the trajectory I see for this technology.

EL KALIOUBY: Yeah, a good mentor of mine, Peter Di Menez, has this quote that in a decade there’ll be two types of companies or organizations — those that have adopted AI and those that are out of business. And I really believe that.

PINEAU: Too. Yeah.

EL KALIOUBY: All right. A few rapid fire questions. What’s one way you’re implementing AI in your everyday life that you can’t live without?

PINEAU: I’m still very much go-to AI whenever I’m stuck on anything. If there’s something I don’t know how to do, whether it’s fixing something at the house or doing something at work, if I don’t know how to do it, I just go straight to AI to unblock myself.

EL KALIOUBY: How are your kids using AI?

PINEAU: I have four kids who are sort of 16 to 22, covering late high school through university. A lot of help with homework — pretty standard teenagers. This week it was also a lot of help writing cover letters for summer jobs.

EL KALIOUBY: Okay. That’s a good use case.

AI as a thought partner in professional matters, yay or nay.

PINEAU: Absolutely.

EL KALIOUBY: And what about personal matters?

PINEAU: Yes, also, though I tend to turn less to it. I tend to turn to my friends and family before I turn to AI on personal matters. I’m not against it.

EL KALIOUBY: Great. Thank you, Joelle, for joining us. This was super fascinating.

PINEAU: Thank you. It was a pleasure to chat with you.

EL KALIOUBY: It was so fun talking to Joelle about her take on where AI is headed – especially digging into OpenClaw.

So here’s my take: OpenClaw feels like the “ChatGPT moment” for AI agents. We’ve all been talking about agents for the past year but this is the first time we’re seeing them broadly adopted by everyday consumers.

What makes this even more notable is Moltbook, a social network where agents can interact and even learn from each other. Again, this is the first time we’re seeing AI bots communicating in the wild at scale.

That said, it also surfaces real concerns — a lack of security and privacy, plus the clear need for human oversight. Personally, I’m not going to download it. Too much risk!

The mainstream version of this technology will need to be a lot safer. It will ALSO need to be packaged in a way that’s accessible and not as techy. But this is definitely headed in the right direction in terms of what AI agents can do for us.