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

27:31And 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. On the team building side, obviously, Canada has great talent. You mentioned obviously some in London as well. What have been your biggest lessons, observations on team building in this talent frenzy that we're in also? How do you analyze that? 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 like a sense of like, what can we create?

28:18Just 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 the sense of like who needs what to operate well and who are that social glue.

28:56Humans are still social beings and that social glue in a team matters a lot. 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. So you can't just buy the Galacticos? 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 that that power that you get from 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 if you don't need to buy the galacticos why do you have like an andrew tallocker daniel gross and alex wang and the galacticos assembling Is that wrong?

29:55You 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 like a team with complementary skill as well. Does that create a good team? Like if I gave you$10 billion to go build a team and you could buy a couple of these luxury star players, I feel like it's top chumps cards for sports teams, but you can buy a couple. Does that create a good team when one is a$3 billion person and then the rest are just average$50 million people?

30:38Yeah, 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 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 higher roster of superstars without being thoughtful how they're going to work together so you it's so funny so because of the impact that you can have in these teams actually the multi-billion dollar price tags that you see can even be justified?

31:20Time will tell. I don't think it's necessarily needed to go at that scale, but time will tell. If I gave you$10 billion, what would you spend it on first? 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. So many things to unpack there. Do you feel you have sufficient compute today? I think we are reasonably well resourced in terms of compute and building the models that we want to build.

32:08So access is not a massive problem? No. Why is data becoming more expansive? Data comes 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, the easy tasks the AI can do. So we're getting in a space where we need more specialized tasks. So 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.

32:47When 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? And so that generation of environments and benchmarks and dynamic domains can be pretty expensive, too. When you look at the expansive data and then you said, oh, cat, dog, lamppost, got these caps, you know, click the ones.

33:27I get them wrong. I legitimately get them wrong. I'm like, Jesus. They're getting harder. They're getting so hard. They are. It's not just you. The other day I called up my CFM. I failed the Revolut. I'm so sorry. I'll try again in half an hour. Please let my A &H agent answer that one for me. Obviously, it was embarrassing. But the question that I have is, you know, when you look at McCall, when you look at Sert, when you look at Turing, how do you evaluate that market, which is providing a lot of that talent? is that an ongoing enduring market or is that just uh hey for the next three to five years we'll need it in the training phase of these models but i don't know what it looks like beyond that 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 human provide guidance to machine we are in this for a long time what But wheelchair changes the nature of the information that the AI provides versus the information that the humans must provide as a complement.

34:30Some 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. It's super interesting. I kind of, as an investor and one of them, see all of them converge around like needing to do three things now. They used to just kind of be talent acquisition. Oh, we'll get you these people. and now they're like we'll get you these people and we'll get you high quality data that you can really use and now it's like oh shit we need this third pillar which is we'll also help you implement that data into your models do training and help you with benchmarking and proving that it's actually and now they need all three are you seeing that third one where it's like implementation of their data as well they don't just hand it over the fence there's definitely some of that that's happening.

35:15I think for me, the even bigger trend we're seeing is the moving from just labeling data to crafting environments to produce new tasks. You said about synthetic data and that also being a very important segment to consider. Do you get model degradation when you get this kind of reinforcing loop of models learning on synthetic data, which creates more data for synthetic and it actually degrades or does it improve? 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.

35:52And that degradation is due to essentially like a loss of diversity of your data. So, you know, you can make an analogy, you know, 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 there are 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.

36:30And so we can generate tons of synthetic data, not endless because it's a closed 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 of 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.

37:10So 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. Do you worry that we are creating a world with just much worse code? A lot of people are concerned by the quality of code that's being outputted and actually how we're just relying on it pretty haphazardly. Do you worry about that? Let me make an analogy in terms of like the quality of generation. You know, 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.

37:46We 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 in terms of the quality of the image generation. So you think of code generation like right now, we're in the phase we were for image 10 years ago. Yes, there's a lot of bad code that's getting generated. There's a lot of code that will get thrown away. but wait another 10 years and I think the quality of the code that's produced is going to be excellent. What will the developer world look like in 10 years when that is the case?

38:22If 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 getting 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.

39:02Someone 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? So it's like a chief curation artist. Yes. Within each company. Curation doesn't go away. Curation, verification. This is work that doesn't go away. Does the structure of Teams fundamentally change then? It's funny kind of playing that back to you and then also playing back to what you said earlier about the human. If that is the case, there's not much of a partnership, is there, between human and AI. It's a chief curation person sitting on top of a huge amount of artificially created code.

39:37Well, that's your 10x productivity improvement there. It is, but you're ticking that box, 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.

40:18Do you think prompts and the way that we interact today with prompts and with chat largely is the enduring interface for human engagement with AI? 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, you know, 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.

40:58So 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. You know, language, words are symbols that encode so much information so efficiently. And so I don't think we're close to getting away from that. When you think about what you did believe that you now have changed your mind on, what's most prescient? Oh, I'm a scientist that is happy to be proven wrong anytime, as long as there's new evidence.

41:37I'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, 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.

42:16Every time you change the size paradigm that neural networks were the first thing we tried, because they're a universal function approximator, and then something else comes out that was better. And that was true for the previous generations. You know, some of you may remember SVMs as like being better than the neural networks in early 2000s. They seem to be quite wrong on this one. Like 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. What does everyone else believe quite strongly that you think they are quite wrong on?

42:49I 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, you know, winner takes all, you know, 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, you lack scientific rigor to analyze these kinds of scenario. I'm 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, you know, making up science fiction scenarios.

43:35You've been on the most incredible, you said there about kind of image generation 2015 and how much it's improved. We're seeing this kind of unbelievable capital supply go into the space in a way that we haven't seen, obviously, for many, many years. Is it a good bubble where we are getting incredible improvements and it's fundamentally advancing technology? Or is it a bad bubble where costs are becoming too exorbitant, teams are too impossible to build, computers too difficult? Is it a good bubble or a bad bubble? 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.

44:15And so there's a lot of variants 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, you know, risk taking new enterprise, new ideas. there's a ton of exciting new startups being created we should continue to to support them you just have to be tolerant to risk i've had some people on the show suggest that evals are to put it delicately bullshit uh and that they don't actually mean anything anymore and like you know humanity's last test like what does that really even mean uh and we have these new tests that come over what is this and leaderboards what is this is that fair or do you think they actually serve a very effective utility to the ecosystem?

45:01I 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 like what type of models or model are you building? What's the characteristics of your system? And then think of evaluations as like unit test for the performance of your system. And software engineers will know what that is, right? Like you run through that evaluation and that gives you like a signal of how the system is doing in a particular dimension.

45:40But as we're building systems that are more and more general, do very specific tasks, you don't optimize for these. We build AI systems that go into enterprise. None of our clients ask about like, are you able to win the math Olympiad with this model? That's not what they care about. They care about bringing value to their business. Now, we're curious to know how well we do on math problems, because it can be predictive of behavior on other things. But you don't obsess over specific benchmarks, you kind of look at the ROI in terms of what you're trying to build. We mentioned there about kind of access for enterprises, enterprises have money, and that's a great luxury in a lot of cases.

46:17Research institutes, universities often don't. With the kind of bubble-like tendencies. People with money are able to afford to compute the talent. Are we seeing this kind of lack of access or democratization for great institutions that are educational maybe, who now can't afford to compete in this new world? Certainly, a lot of universities have a lot less resources than companies today. That's not completely new. When I joined Meta 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.

46:54But, you know, there's still amazing research that's being done in universities. You go to the major international conferences, Nureps, 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. And what's actually especially good is talent flows between them.

47:34University 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. How important is it to have seen success and how valuable that makes you? When you look at people like Amira raising 2 billion at 10 billion, it's like, well, no one's seen the success that she's seen with OpenAI, so it's valid. Help me out as an investor. Is it that valid to place that much of a premium on access to seeing it at that level that we are or is that slightly overpricing it?

48:11I think in many cases you know when it comes to deciding where to invest very early on when you don't have tangible 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 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. I don't... If you were investing today and you were joining my team, which category would you most like to invest in?

48:49Be it security, be it generative AI, compliance, you name it. 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. That's very exciting on the healthcare front in particular. When you think about that timeline as well, I'd love to do a quick fire round with you if that's okay. So I say a short statement. What would you most like to do, but because of technical or financial limitations, you're not able to?

49:23I'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. When you think about that ecosystem of agents, you have children. Yes. And AI changes our relationship with other humans and friendship and social. How does AI impact social friendship connection? It definitely does. And, you know, there's a sense that we spend a lot of our time in the digital world.

50:07And for some folks, you know, 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. It's 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 for that social element knowing what you know what do you not let your children do eat too much sugar totally that's so that's that like physical diet completely agree with that yeah is there a technical diet 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 we're going to discuss settings I know that was not a popular one did they listen the thing with children is you don't know till later do you limit screen time I spent a lot of energy especially in their younger years limiting screen time my kids did not have a cell phone till they were 14 15 Did you see adolescence?

51:21I have not. Okay. Watch it. Basically, a little boy goes up to his bedroom and gets lost down kind of rabbit holes of TikTok and Reddit and it does not turn out well. I've heard about it. I just haven't had time to sit down. Do you worry about the loneliness pandemic and then also just the mental health crisis that we have? 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. You know, there are a 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.

52:08And there's a lot of people looking for shortcuts and short answers, but I think more research into that is definitely warranted. What's your biggest lesson from working with Zak? He is incredibly deep into understanding the work. 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. And that then just informs everything he does after. So you can have the most amazing team, but as a leader, you need to go deep and understand the work. Did you see him change? As anyone gets more knowledgeable about a topic, you get more decisive.

52:50There'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. What one AI buzzword would you ban if you had a magic wand? Existential risk. Why? Because it just makes people afraid. And it's not out of fear that we make our best work and we take good decisions. Do you find the cost of talent exorbitant? Talent is costly. Talented 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.

53:29Final one. What are you most excited for? You don't like the existential risk. I don't like the doomsday planning. When you think about the positivity that can come, what are you most excited for when you look forward to the next three to five years? I do think some of the work in terms of AI for a 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.

54:10No 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. You know, we were in the frenzy of large language models. And I pulled the stats on, you know, 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 to be able to do at the scale that runs on like one or two GPUs. Final, final one. You said there about kind of open. We seem to like be reverting to a closed world now.

54:47Is that the world with which we should predict and plan on? 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 like close us down is absolutely false. I mean, people are circulated. Do you not think we are though moving into that world? Everyone seems to be closing systems, closing access. 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.

55:24This has been such a joy. I've learned so much from this conversation. Thank you so much for putting up with my very basic questions. But I've loved having you on the show. My pleasure. Thank you. but before we leave you today are you drowning in ai tools chat gpt for writing notion for docs gmail for email slack for comms and you're constantly copy pasting between them all losing context and losing time this is the ai productivity tax and it's killing your output at 20vc we're all about speed of execution and superhuman is the ai productivity suite that gives you superpowers everywhere you work.

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