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Joelle Pineau

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2025-11-03
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2025-11-03
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  1. There are definitely a number of places people that are closing down access. I don't think that is going to be effective. Ideas will circulate. And I also think it's a mistake from a point of view of fostering innovation.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  2. That's a deep mistake. I mean, I will continue to believe that, especially for research, the ideas need to circulate. And this thought that you can just close this down is absolutely false. I mean, people are circulating.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  3. I do think some of the work in terms of AI for scientific discovery is going to be pretty fascinating to see just in terms of the doors it's going to open up the ability to explore combinatorial space of solutions. So I'm curious about that. And then I'm super curious to see how can we actually make our models more efficient. There's larger and larger and larger models. No one wants to run these models. You know, I spent a lot of my career building open source models, and I'll give you one example. We were in the frenzy of large language models, and I pulled the stats on most downloaded models of last month. We had a model like Roberta from 2019, small language model was getting 20 million downloads a month. People want efficient models that they can use, that they can run. So I'm also super keen to see what we're going.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  4. Talent is costly. Tala did people deserve to be paid well. And again, you know, someone coming in just because of money rarely is going to be the right person, but you do need to compensate people fairly.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  5. Existential risk Because it just makes people afraid. And it's not out of fear that we make our best work and we take good decisions.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  6. Is anyone gets more knowledgeable about a topic you get more decisive? There's a phase where you're really learning and trying to understand, and there's a phase where you understand a lot of things, and then you make your decisions faster. So certainly that shift happened.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  7. He is incredibly deep into understanding the work. Like he does not coast. When he started getting into AI, just the depth of the question that he'd ask, he just gets really interested in the topic and goes super deep in that then just informs everything he does after. So you can have the most amazing team, but like as a leader, you need to go deep in understand the work.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  8. I do worry a lot in general about making sure that people are healthy mentally. We have to be careful about taking shortcuts and saying, you know, because suddenly, you know, we have certain platforms, we have AI and so on. That is causing that mental illness. number of people who are suffering and they deserve to have good answers to the situation and they deserve to we deserve to find real solutions to that and there's a lot of people looking for shortcuts and short answers but I think more research into that is definitely warranted

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  9. I spent a lot of energy, especially in their younger years, limiting screen times. My kids did not have a cell phone until they were 14, 15.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  10. I spend some time discussing like settings. I mean, like you get an Instagram account, great. You can have an Instagram account, but like what are the settings on that account, making sure they understand? I mean, they'll go and change them if they want.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  11. It definitely does, and there's a sense that we spend a lot of our time in the digital world. And for some folks, I look two of my children, they spend a lot of time in the digital world playing online games with their friends. It's still very social. There must be some AI. There's the digital platform, but it's still a very social experience. Others have more individual experience. There's definitely a shift of this time we spent towards that platform where we go look for that social element.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  12. So a I'm super keen to figure out how we build societies of AI agents. We're doing it implicitly, but how do we look at populations of AI agents interacting together and having like a sandbox for doing that? Maybe something I'll get to do is it lack of time, resources, something else. There's just like a ton of different things to do, but keen to see what happens there.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  13. Yeah. There's a lot of verticals, whether healthcare, scientific discovery, that I think have incredible promise where we're going to see real tangible progress within five years that are going to change completely the face of what we can do. So that's probably where I'd push.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  14. I think in many cases, you know, when it comes to deciding where to invest very early on when you don't have tangible information, I mean, you look at people's track record and there's a part of that that maybe like, you know, what have they learned in terms of the core recipe? But the other thing is also the achievement of having put together amazing teams who are building world-class models. And there's a lot of subtlety to that. And so I think, you know, both of these ingredients are important to consider.

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  15. System and what's actually especially good is talent flows between them. University students come in, do internships, take jobs at companies. We've also seen a movement of people coming out of these large companies going back to university teaching, sharing with the next generation what they've learned.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  16. Certainly, a lot of universities have a lot less resources than companies today. That's not completely new. When I joined MEDA in 2017, one of the reasons I did that is because I could already see the disparity in terms of access to compute. And I was really curious to see how you could do research with a lot more compute. But, you know, there's still amazing research that's being done in universities. You go to the major international conferences, NERPS ICML and others, and often the best paper awards are actually won by researchers out of universities. There's a lot of good ideas that you need to test out at small scale. And in a university, you have a lot more freedom to pick pretty risky ideas at a small scale, but still, you know, no one's asking you to justify your research in ways that often happens in companies. So I think they play different roles in the ecosystem.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  17. I do think they are really good indicators. So I think you do need to take evaluation seriously in terms of knowledge, but you shouldn't take them seriously in terms of the ultimate goals. So, you know, evaluation, and there's lots of different benchmarks and so on, you have to decide what type of models or model are you building, what's the characteristics of your system, and then think of evaluations as like unit tests.

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  18. I think about it as a bubble with bigger variants. It's like, you know, the upswing is going to be bigger and, you know, there's going to be big downswings as well. And so there's a lot variance into the system right now, as long as people have a tolerance to risk. Then I think AI is a great investment and we should continue to be supporting risk taking new enterprise, new ideas. There's a ton of exciting new startups being created. We should continue to support them. You just have to be tolerant to risk.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  19. I don't have a lot of patience as a scientist for people who are predicting sort of the extremist scenarios, whether it's the catastrophic risks of AI or whether it's the winner takes-all AI becomes our overlord kind of scenario. I don't have a lot of patience for that. I wouldn't say it's necessarily widespread, but I just lack scientific rigor to analyze these kinds of scenarios. I'm a much more pragmatic grounded. I'm pro-innovation. I'm excited to see where AI is going and the problems it can solve. But I'm not so interested in just going around and making up science fiction scenarios.

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  20. You know, some of you may remember SVMs as being better than the neural networks in early 2000s. They seem to be quite wrong on this one. Neural nets seem to be here to stay and the ability to do backpropagation and gradient descent and all that seems to be a really powerful way to learn.

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  21. I'm a scientist that is happy to be proven wrong anytime as long as there's new evidence. I'm genuinely curious to know other scientists are much more like holding on to very, very strong conviction. I have weak conviction but very strong respect for the scientific method and rigor, experimental rigor, you know, theoretical rigor as well. I used to be quite skeptical that neural networks were necessarily the ultimate solution to machine learning. I'd seen enough cycles of neural networks kind of peaking and then being less useful. And I used to think every time you change the scale of the data, you know, you go from hundreds of examples to thousands, thousands to hundreds of thousands, to millions of examples. Every time you change the size paradigm that neural networks were the first thing we tried because there are universal function approximator and then something else comes out that was better. And that was true for the previous generation.

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  22. It's awfully limited, and prompts can mean a few different things, but the idea of like typing in a box, that to me is very limited. And we're going to break out of that box already. We're seeing a lot of cases where voice is a lot more natural as an interface. I do expect we'll see gesture, eye gaze, these kinds of much more multimodal ways to interact with the AI rather than just stick in that prompt box. But language is incredibly powerful. So if you think of prompt as being more language as a way to express ideas and communicate with a machine, that's a powerful paradigm. I mean, as humans, so much of our communication is based on language. I don't think we're going to move away from that. It encodes information. Language words are symbols that encodes so much information so efficiently. And so I don't think we're close.

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  23. But it removes a human element there. You still need people with intent. That's one thing that, you know, you need to decide what you want to build and what purpose does it serve. And so that intent is still there, that role of critique is still there. So the team composition does change significantly once you suddenly have designers who in their hand have amazing tools to go directly from the ideas in their head to the digital world, maybe eventually to the physical world, that equation definitely changes.

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  24. If I carry my analogy further, I don't know if it's a reassuring scenario because if we look at where we are today in terms of image generation, there's just like the volume of image generated is huge. What matters now is sort of, you know, picking the quality out of the volume. And so if I fast forward 10 years on code generation when we have the ability to generate a ton of code to do a ton of different things, we're going to need some selection mechanism to decide what code we actually want when there's actually value. And so that's going to come. There's still going to be some sort of editorial design choice. Someone needs to decide. Like of all the code we can generate, what's the code we want to generate? What do we need to be running in terms of our digital world?

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  25. Let me make an analogy in terms of the quality of generation. You ask about code generation, but let me take you back to 2015 and image generation. I don't know if you have it in your mind, but the quality of the images that were generated, we had image generation models in 2015. They were really bad. The resolution was bad. The composition was bad, and so on. And from 2015 to about 2022 or so, we saw huge progress.

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  26. World, but still tons of synthetic data, and through that learn for a long time. Then there's domains that are sort of in between. If I think of coding, we can generate synthetic code. You take normal code and we know how to inject diversity into the code. Like I can take a couple repositories, mix and match, apply an LLM to transform it. And so there's a way to generate synthetic data. The language is predictable enough and there's enough structure that I also know how to inject diversity so that you don't get that collapse. So the hope is that especially in these domains we can use a lot more synthetic data and do it without suffering from the degradation of performance.

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  27. It really depends how you're generating your synthetic data. So, in some domains, if you think like images, languages, like LLMs talking to each other at some point, you definitely get the degradation and that degradation is due to essentially like a loss of diversity of your data. So, you know, you can make an analogy. You take a bunch of people, put them on an island, and let them reproduce, you know, at some point the genetic diversity is going to keep shrinking. And so you get a reasonably similar phenomenon with models because you're not injecting diversity into the data. So for their domains where lack of diversity means you get a collapse of distribution. There's other domains where you don't need diversity if you think of like playing chess, playing go, these kinds of games. We know exactly how to generate board configurations. And so we can generate tons of synthetic data, not endless because it's a closed.

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  28. There's definitely some of that that's happening. I think for me, the even bigger trend we're seeing is the moving from just labeling data to crafting environments to produce new tasks.

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  29. I don't think it's a phase in the sense that I do think this partnership, we'll call it, between humans and machines where humans provide guidance to machine. We are in this for a long time. What will change is the nature of the information that the AI provides versus the information that the humans must provide as a complement. Some of these firms may not be around in five years, but this notion of having humans guide the behavior guide and train the behavior of AI system.

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  30. Data comes in in different forms. On the one hand, the days of having data labelers who can say this is a cat and this is a dog are somewhat over, like the easy task the AI can do. So we're getting in a space where we need more specialized tasks. So, you know, imagine you're building AI for enterprise. There's a particular business logic. You need to make sure that you're catching the errors. You're going to need someone with deeper understanding of the tools so that's more expensive talent to come in and actually prepare the data. There's also a lot of data that's synthetic data. When you're building agents, you need to build environments. And to build environments, you need some pretty creative folks who are going to build you like synthetic simulators. We've seen this on the robot side for many years, people building robot simulators. Now you're building AI for enterprise, so you need to think of like, how are you going to simulate these work processes in a reasonably realistic way that the AI can train on that?

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  31. One of the things you need is a balance between talent and compute. If you have too much talent and not enough compute, you're wasting your time. Usually like an equilibrium between those two pieces. I think we often underestimate the importance of data and data is getting more and more expensive. And so I would certainly spend a good chunk of it on data as well.

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  32. I wouldn't say no if someone offers me the opportunity to hire. There's definitely some really talented people in the field and they deserve to be fairly compensated. This technology is going to have make a lot of people very rich and have major effects in terms of society. And so, you know, we should be rewarding the talent. But I'd be very thoughtful about what are the teams that I put together and how do they work together rather than just like hire roster of superstars without being thoughtful of how they're going to work together.

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  33. You do need a few of these like Uber talents in the team. There's a relatively short number of people who just understand this technology very deeply. You do need some of this talent and if you can afford it, you should get some of that talent. But you don't need all of your team. You need a team with complementary skill as well

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  34. I don't think you need to. I think you really have to be thoughtful about putting people in a group. The other thing that helps a lot is for the team to have focus. You know, if it goes in all sorts of different direction, you'll lose that power that you get from people working together. So having a lot of clarity, what's the North Star? What's the goal? Where are we going? Even if over time that needs to change. But that level of clarity is required for everyone to be working in the same direction.

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  35. Where I've seen it fail is to have just sort of one type of person inside the team. So I'm a big believer in building teams with diverse complementary talents.

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  36. One of the things that's important when you're building a team for AI, I do think you need people who have vision, who have a sense of what can we create. Just because we're in a space where there's so much innovation that is still needed. So you need an ingredient of vision that can be one, two, three people who bring that ingredient of vision. You need people who have amazing execution muscle. They don't care that it's their idea. They care that if the team agrees on an idea, they are just going to push this and get it done. They're going to build a system. They're going to run the experiments. They just have that technical rigor to execute. And then you need people who kind of like keep the team together, who have like this sense of like, who needs what to operate well and who are that social glue. Humans are still social beings and that social glue in a team matters a lot.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  37. Fits all solution. You know, I go back to the research we've done. We've done leading work in terms of multilingual model. And it turns out it matters. You go to Japan, you go to Korea, and they do want models that work well in their language. People in the workforce are still operating in the language of the country. So having a company that is attuned to that, that values, that internationalization of model is actually important on the global market.

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  38. I do think it's healthy that there are models that are getting built in different places around the world, not just in the US and China right now. I think this is healthy in terms of diversity of thoughts. I think it's healthy in terms of having a greater amount of people with access to technology. I do think for Cohere, you know, sufficient isn't to be a Canadian company. Like the vision is to be a global AI company. And I think yes, you know, we have a headquarter in Toronto. We have teams that are distributed around the world. We have a great team here in London, as well as in the US and France and other places. And so having that ability to deploy models that operate across the world, I think it's going to be absolutely an important part of the strategy for cohere. I think there's a great opportunity. What it gives us to be headquartered in Canada is like a sensitivity to the fact that it's not always a one-size-fits-all.

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  39. Ahead of the technology, I think that would be the wrong order of things. In some sense, we need to develop that technology with enough of a creative space. And we need to learn fast and then develop the right guardrails for that technology from the real earnings we have.

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  40. I don't think you should look at where government are in terms of necessarily AI regulation, that AI as a field is so incredibly young and fast moving and by nature. And there's some good in this. Governments are moving a little bit more cautiously and usually need to benefit from our knowledge to make good policies. And so I do think you can look at other fields in terms of regulation. You know, you look at aviation, the security record for aviation today compared to where we were 50 years ago is just incredible. And governments have played a role in defining that in terms of standards and in terms of what are the norms and so on. So I'm quite hopeful. I'm an optimist about this. Maybe it's my Canadian side, that governments can play a useful role in many cases, you know, clear standards actually means reducing uncertainty for a lot of companies in this space. But we shouldn't expect that to be.

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  41. Governments can be good for defining standards on which we all agree. Companies are much better at building the solutions at scale and deploying them.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  42. In terms of agents, we worry a lot about hallucinations in LLMs. The parallel in agents is impersonation. So agents that come along and are essentially impersonating entities which they don't legitimately represent in doing so, taking actions on the behalf of these entities where they don't legitimately represent, whether it's infiltrating banking systems and so on. And so I do think we have to be quite lucid about this, develop standards towards that, develop ways to test for that in a very rigorous way. There's ways to reduce that risk drastically. You run your agent completely cut off from the web. You're reducing your risk exposure significantly, but then you lose access to some information. So depending on your use case, depending on what you actually need, there's different solutions that may be.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  43. With respect to AI security, I think there's a new front that's opening up with the development of agents. And frankly, there's a lot we don't know yet in terms of the vulnerability of these systems. With LLMs, we're starting to get a better understanding. We've had quite a bit of red teaming exercise and jailbreaking and so on. And so people have identified different risk vectors. prompt injections, things like that, which are vectors for malicious actors to interfere with a system. With AI agents, we haven't seen that. And one of the features of computer security in general is often, you know, it's a bit of a cat and mouse game, quite frankly. Like there's a lot of ingenuity.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  44. And that's, you know, of course, that's something we see a lot of cohere because we do on-premise deployment. One of the things we focused on the most is data confidentiality and security so that enterprise can exploit all of that information. So that's top of mind for us. But it's also a huge opportunity, I would say. There's a big interest in that. But making sure to get that compatibility, I think, is a challenge. In many cases for people, change is hardest for people. And so you have to get them curious about using the technology. Many people feel they have to get it right the first time. And I really think like a spirit of exploration and curiosity is much better suited to the face of maturity of the technology that we have today. We don't have all the answers of how it should be used. That's going to come from people on the field.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  45. Know one of the challenges is to make sure that the AI comes in and can be integrated in their workflows, their processes, their information. And so the challenge is to deploy in a way that allows them to exploit all of the information systems that they already have and some of them have accumulated these over decades. you know, some of the work that remains to be done of figuring out how to do that.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  46. I see a lot of people using it as a tool more than as a companion. People have this Swiss knife in their work life all of a sudden that can be super helpful, but that's really most of what I see.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  47. A lot of the workforce can be reasonably fearful about job displacement. There's a lot of also individuals who have a bit of an instinctive reaction to change. And change can be hard for a lot of people. And we're seeing a lot of change in a very short time span. And so I think there's also a generational effect. I think for some generations that change is more jarring. I think for the younger generations, teenagers, young adults at home for them, it's just native. They just, you know, kind of, you know, going to grow up with that technology in a different way than some of the older generations.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  48. It goes back a little bit to this notion of what are the tasks that we can specify well. In any case where we can be very precise about what a great result looks like, we'll be able to make that task automatic much more easily than tasks that are much more nuanced and have a lot of complexity.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  49. That correct, yes. You know, there's a lot of nuance to all of that, and some work will be harder to get that same level of efficiency gain, whereas other work, you'll see 100x in terms of efficiency gain. But I do think that for a lot of the work that's happening right now, that's absolutely feasible.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source

  50. I think you have to identify very concretely the types of work that you are delivering, but I think we're starting to see Hollywood quality productions being made in a matter of hours. We're seeing, you know, to take a super concrete case like machine translation, if humans are doing the machine translation compared to machines doing it. You go from hours to seconds on long form texts, multi-page documents. And so for a lot of work, it's not like AI can do all of the work. Humans still need to ask the right question. They need to verify the information. They need to shape the tasks. But once the task is well-defined, the product are clear, like all the design considerations are fed into the prompt, you press the button and you've got an answer in seconds for something that used to take sometimes weeks and months.

    2025-11-03 · The Twenty Minute VC · 20VC: Cohere's Chief AI Officer on Why Scaling Laws Will Continue | Whether You Can Buy Success in AI with Talent Acquisitions | The Future of Synthetic Data & What It Means for Models | Why AI Coding is Akin to Image Generation in 2015 with Joelle Pineau · IDENTIFIED FROM THE TRANSCRIPT · source