Coursera, Inc. (COUR) Earnings Call Transcript & Summary

All right. We will get started here. Thanks for joining this fireside chat that we're hosting with Andrew Ng. I'm Stephen Sheldon, and I'm an Equity Analyst at William Blair, covering vertical-focused software and services including the education technology space. Andrew needs very little introduction as one of the most influential thought leaders around artificial intelligence and machine learning. He wears a lot of hats, I think as most know, and some that stand out includes extensive Baidu and Google, helping out with various AI initiatives and leading various AI initiatives. He then founded deep learning AI back in 2017. He's the managing general partner of the AI fund that was started in 2018. And earlier this year, was appointed to Amazon's Board of Directors. And most relevant for this conversation, he is the Co-founder and current Chairman of Coursera, while also continuing to -- as a professor at Stanford, something he's been doing for over 20 years now. So we really appreciate him spending time with us today as we dig into his views on the future of AI, its potential impact on the broader tech landscape along with the potential impact on the education ecosystem and Coursera's positioning within it. So thank you so much for the time today, Andrew.

Good to see you, and thanks, everyone, for dialing in to watch this.

Well, just to start, I think, just given your position in this full ecosystem and the visibility you have into the broader AI ecosystem, can you talk some about the more transformative applications of generative AI that you're seeing today, especially those that might not be as widely known? What are you seeing out there?

So one thing about that question is it's almost hard to pick. One of the things by AI and generative AI is a general purpose technology, kind of like electricity. If I would ask you, what is electricity good for, it's hard to answer that because it's so pervasive. The generative AI feels like that as well. And I think my team, AI Fund is finding applications and tons of industry verticals. So using it to process tricky legal documents to deal with complex government compliance, has obviously many applications in education and ed tech, using this as medical diagnosis. But I think a lot of these applications of generative AI are little bit nascent. So maybe they aren't as widely known, things like ChatGPT and Gemini and Claude are widely known, some things of AI assistive coding, that's really taking off, that's more widely known. But I'm seeing so much -- so many green shoots in so many different industry sectors that are sometimes B2B rather than B2C. And so the word also doesn't get out as quickly but I'm seeing many applications being built with pretty inexpensively frankly, that are starting to deliver ROI in a very wide range of industries.

And what -- I guess, when you think about -- I guess, what has surprised you the most about the way generative AI is being leveraged today across industries? Are there -- and are things generally moving faster or slower than you would have expected 2, 3 years ago? How fast are things moving?

It feels like the hype has been more than I would have expected. And I think that the progress feels good and steady and healthy, although for some positive, maybe there was more hype that will take a little bit of time for everything to catch up to. Maybe I want to share what I think might be the one common misconception about AI, which is people still think it's very expensive to build with AI. And it turns out that -- well, this is how I think of as an AI stack, right? At the lowest level, it's a semiconductor layer. So clearly, NVIDIA's doing well, AMD has strong offerings and so on. On top of that other clouds, large clouds. And then on top of that are the foundation model companies like OpenAI, Anthropic, [indiscernible]. And it turns out that whenever there's a new technology, these technology providers, semiconductor cloud, maybe AI model, foundation model trainers, these tend to grab a lot of the headlines, and that's fine, nothing wrong with that. But it turns out -- and it turns out that some of these layers, training large AI models, it does costs billions of dollars. So it feels very expensive. But almost by definition, there's one layer of the stack that has to do even better, which is the application layer, and because others have spent billions of dollars training these AI models, what is not as widely appreciated is that the cost of experimentation of using a model that someone else spent hundreds of millions of billions of dollars to train to build a new application on top, that is much more capital efficient than most people realize. So my team at AI Fund budgets $55,000 to get to working prototype to often tackle a major industry, right, application. And I find that, people probably don't know, I probably still code, there is a lot of time to go, but I still do. But when I build prototypes, it often costs like tens of dollars to get a working prototype in the API calls, and this is causing both large corporations as well as start-ups to have an innovation engine that could be operated very differently, very unlike anything I've ever seen before because you can prototype and test and frankly, fail very inexpensively. So I do see corporations now even explicitly taking a strategy where they say, "You know what, let's build 20 things. Take us 10 days to build each of them. And if 18 of them fail, but 2 turn out to be valuable, that's great." So this is actually changing the way that corporate innovation as well start-up innovation is being carried out.

Yes, that's fascinating. Maybe just in that context with the pace of innovation. Which industries do you think could be the most impacted as we think about the next 5 to 10 years due to AI's proliferation? Are there some that are likely to be massively reshaped as we think about that time frame?

Yes. I'm going to give that answer as my answer. I think it's all of them. And maybe -- there's a research study by some friends in University of Pennsylvania and some of that collaborators at OpenAI that analyze different types of work and their exposure to AI automation or augmentation. And it turns out that whereas early ways of automation tended to affect routine repetitive work, think factory automation or industrial automation, generative AI is much better at knowledge work. And so with this wave of transmission, it tends to be the higher wage jobs that have more tasks that are amenable to AI automation. So maybe in terms of the industry sectors, I think we'll adopt generative AI faster. I think it's more the knowledge work industry this time compared to the industrial automation sectors. And then I think also the sectors that are more digital will tend to adopt things faster because of the DNA, the IT personnel. One interesting thing that's happened over the last decade is everyone has become -- almost all industries have become much more digital than they used to be. So I think 10 years ago, when the last wave of AI technology, deep learning, predictive AI supervised learning started to take off, there was a huge gap between the more digital industries like financial services, maybe health care, and the more traditional industries like natural resource extraction. There was a bigger gap. But it feels like in the last decade, everyone has become more digital. And so the pace for the leaders and the laggards, there's just less of a gap. But I do think the more digital industries, things like finance, some positive health care will probably still adopt faster than some of the more traditional industries with a lot of moving atoms around much more than moving bits around.

Got it. And maybe this is kind of an education focused, a certain degree focused conversation. There are predictions that education could be one of the industry's most impacted. I guess how are you thinking about it? Do you agree with that?

I think many of us, and I do, too, feel like there could be a meaningful exciting transformation of education coming because of generative AI. I don't feel like I know exactly what it will be at this coming transformation. But folks at Coursera and more broadly around are certainly thinking hard and experimenting and prototyping. Maybe just to share with you some things. A couple of things that Coursera has done. I think were actually really nice are Coursera Coach, kind of like AI-TA that chats with learners, customer responses. It really helps learners. The data seems to be that they are really good for learners that the AI-TA answer questions. Hope you do the learning. I think for a lot of learners, if you are studying something and you get stuck, maybe you try to write a piece of code, and you don't know how to move forward. You are actually stuck potentially for a long time until you find a human expert to get you unstuck. With the AI-TA Resistance Coursera Coach, you get unstuck right away. Coursera also built a product called Course Builder, which we think is actually really nicely done where a lot of Coursera's enterprise customers are very interested in -- if there's a 3-hour course, but you don't want a 3-hour course for your employees, can we cut it down to 1-hour course, and maybe have the CEO contextualize it to make it more relevant for a specific business. So I think we're going to take content into custom courses. It seems like exciting step forward as well, just proved out to be very useful right now. Having said that, I think these are just early designs of what will be possible things that will be even more possible and even more exciting in the future. So actually, Mustafa, Coursera CTO, CPTO, Product and Technology Officer, he really does a nice job running tons of product innovation. So cautiously optimistic, there'll be a lot of new things to be invented. I think it would be an exciting sector. One thing about Coursera's DNA, very loyal to learners. From day 1, we always said, let's put learners first. So part of the team DNA is to stay connect to the learners, really think through what learners want. I'm not worried about the demand for learning. I think humans want to be empowered and learn stuff. So the societal demand for learning has been large, and it seems like it will only continue to grow. And then I think our ability to invent new things to serve learners. It feels like it'll be an exciting next few years.

Yes, that's helpful. So I guess maybe stepping back, what do you think -- as you think about Coursera's opportunity here, what do you think differentiates Coursera's generative AI capabilities and offerings? And are there particular elements of how you design the content, the platform, the data, et cetera, that you think are uniquely valuable?

So content and platform, I'm not sure. I think Coursera had a good product for a long time. But because Coursera has a relatively large user base, this does give Coursera the ability to see a lot of -- see what a lot of learners are doing as well as get data -- detailed data about what a lot of learners are doing on the platform. And then when Coursera has product innovations, it also makes it relatively easy to have that platform to scale it out very quickly to a lot of learners. I think Coursera has a reputation for being the quality, right, educational being high quality. And I think -- by the way, as I see, I feel like -- I chat a lot with Coursera employees, chat a lot with Coursera partners. One of the things I'm genuinely very grateful for is the degree to which a lot of Coursera team and also a lot of Coursera's content partners, the teacher in the Coursera platform. it's just that loyalty to learners where people really are in it for the mission. And then one nice thing about the Coursera team is, the team has spent lots of time thinking about generative AI. So internally, I want to say pretty much everyone is very facile, we're using large language models. And so there is a lot of product innovation and thinking and then often chats to the team about some of these things, too, about not just what's on the website today, but also a bunch of ideas being experimented with product innovation to serve learners better and then to try to take this to scale quickly on Coursera's platform.

Got it. Makes sense, and that's all really helpful. And I think the focus on the learner experience has been very evident. Maybe shifting to the demand environment. How do you think the expansion of AI could impact demand for Coursera's solutions over the coming years? And maybe from the perspective of both individual consumers that are trying to learn about upskill themselves and leveraging AI and at the enterprise level for the B2B side?

Yes. So I think -- maybe I think there's certainly been a very significant demand for both consumers and enterprises in learning about AI. And in fact, AI technology has evolved rapidly enough that most organizations, frankly, even universities don't have the resources or the capacity. It's hardly express fast enough to deliver the training. So frankly, many universities, even very good universities just don't have enough professors to teach cutting edge AI because the field is evolving so quickly, and the number of experts is still relatively small. So I think that Coursera works with hundreds of content partners, including many of the world-leading experts, really some best companies in AI, inventing the future technology to get the cutting-edge, most relevant, most high-quality and technically accurate content to teach people in many different walks of life. I find that exciting. And then I think maybe at some point, there will be a job disruption of AI where more people are being reskilled. Honestly, I -- hard to say how fast that will come. It doesn't feel like it's happening quickly, but I think the demand to learn AI -- one of my friends, Stanford professor Curt Langlotz said basically, AI won't replace people, but people that use AI will replace people that don't. And I think for many -- I think that generative AI has reached the point where every knowledge worker can get a meaningful productivity boost right now by using generative AI. But one of the challenges is many people will need a little bit of training to use it safely and effectively. So there is definitely significant demand there. And maybe just to share you some stories that excite me. On my team, at DeepLearning.AI Fund, I see a lot more people, for example, learning to code and going really deep in the usage of AI. So for example, I have a marketer, whose job is a marketer, a novel software engineer, he's learning to code. So as a marketer, he writes code to create web pages, download web pages, get marketing insights, have an investment professional IT team that uses AI to help them code, to use -- customize legal contracts more efficiently. But I've seen many people whose job role is not software engineering, nonetheless get a lot of value from using AI in a very deep way to the point of, frankly, writing code. And one of the things that maybe on the personal level, I would love to see happen, that will require a big societal wide transformation is the world will be much richer if we can get everyone to not just be a user of AI but be builder with AI, kind of take control of the technology, really learn in a deep way, maybe to the point of coding. And I'm seeing in my life enough non-software engineers who have learned enough about AI to use it really deeply, drive business results that I think -- I'm hoping this will be a broad trend that we can push out across society.

Yes. That's really interesting. Maybe just on -- you kind of brought it up, but the -- I think there have been some estimates, that 20% to 30% of the workforce might need to upskill or reskill or just generally be retrained just as jobs get more or less displaced to a certain degree by AI. I think you said you don't know when that will happen. Do you think that will happen? And do you think that is an opportunity for Coursera?

So yes, I've seen some of those studies saying 20%, 30% of the workforce. I think it depends on what we think of upskilling. Actually, honestly I tell you [indiscernible]. I was surprised it's only 20%, 30%, not because I think 20%, 30% of jobs will go away. But I think I would love to see closer to reskilling of 100% of the -- closer to 100% of the workforce because maybe when the Internet starts to the work and we've got web search engines and so on, you kind of like everyone, at least all knowledge workers pretty much had to learn to use website. So today, I can't imagine hiring a marketer or investor or HR professional or whatever, that doesn't know how to do web search. So everyone had to do web search, pretty much everyone had to learn to use mobile phones at least in knowledge work. And so I think a few years from now, actually, even today, I can't imagine hiring a marketer anymore that doesn't know generative AI to some extent. But having said this, this is not about all the marketers are laid off and need to be reskilled, some completely different. I think this is people in their current job roles will be much more effective if they know AI than they don't. And then I think there are some sectors where jobs are going away. One of the most heavy disrupted sectors is call center or contact centers. I think there'll be a few other sectors like that. So government, citizens maybe corporations to have responsibility to make sure people are well taken care of. I think so far, the layoffs, fortunately, have been much lower than maybe some of the hype has been. But then I think when someone needs a switch or start a new career, edtech Coursera, I think is a wonderful tool for that. But then at least for the immediate future, I think this -- almost everyone should learn to use AI to make themselves better and more effective and personal professionalize. I feel like the amount of work there that remains to be done in education just seems massive.

Makes sense. I'm just hoping Equity Research stays relevant here for quite a while. But I think you're right, I think everyone is going to need to focus on how do you leverage AI and make yourself more productive. And maybe shifting that into maybe the more enterprise focused. Companies, I think, have been -- you talked about the cyclicality, I think of company spending. Companies have been spending less in recent years on learning and development. I think Coursera and some peers have been talking about stabilization there. What do you think it will take for business spending to pick back up? And are companies starting to think about, I've got this workforce, I need to make them more productive, how do I train my workforce to focus on -- or to leverage AI and specific tests? Are you starting to see that pick up at all?

So there's definitely a lot of companies thinking about how to train up their workforces of AI. In fact, people used to talk about the digital transformation. I hear the term AI transformation more and more, and training is often a key piece of AI transformation. Maybe just to share you, some things I see, one of the challenges of the entire edtech sector is education has, I think, has a profound long-term ROI. I mean when we train someone, provide them new skills, they're materially more productive and more efficient. One of the challenges is I don't think we're as good at measuring the ROI as I wish. And so the challenge of quantifying what I think is just a massively profound ROI, it makes some of the investments a little bit harder to justify. And then I think -- and the other interesting thing is really all of the edtech -- education sector, not even edtech, but really education is when you change someone's life with education, how does that provider of the service capture just a small slice, the massive amount of value that is generated. This is kind of a -- fortunately, the value created is so massive, it's working out okay for the most part. But I think this is an interesting structural problem for education.

Got it. And maybe I think one thing that I've heard come up is just with the expansion of generative AI and content authoring tools as well. The companies could start to in-source more of their learning content by leveraging generative AI and therefore, rely less on external vendors like Coursera or, I guess, even academic institutions. So is that a risk you think about what are you seeing from that whole kind of in-sourcing concept?

Yes. I don't ever want to say something is absolutely never not a risk. And -- but then part of me feels like, boy, I wish it was that easy. So Coursera, DeepLearning.AI, me personally, we spend a lot of time experimenting with AI for content generation. And boy, I wish it was that easy. It turns out that at least right now, with the current state of the technology, it's still extremely difficult to generate high-quality content. Generating low-quality content is easy, but to generate technically accurate thoughtful time efficient content is very difficult. So it turns out, some of our friends have built avatars of me. So it turns out you could do the computer graphics to make something that looks like me. You could do the voice cloning to make something that sounds a lot like me enough to fool my parents sometimes even. But to make it say the right words, to convey technically accurate as well as insightful content, that's incredibly difficult. So the Coursera team, also some folks at DeepLearning.AI definitely working hard. We actually are working hard on that. And I think one thing that would be exciting would be today because content is so expensive to generate. We tend to have to generate content that is then served to a large audience. I think that as the technology improves, it will become economical to hopefully serve smaller and smaller, more and more niche audiences. And the ultimate niche would be a niche of one audience. We can generate custom content just to teach one person. But I wish the technology was that easy. I think this is a very high technical barrier. But with Coursera's existing very high-quality content assets, the team. I feel like, well, I don't know. It will be exciting to see what we can do. And in fact, I think Course Builder is one foray into this, where we use basically high-quality content, but hope customers mix and match it to form their own custom courses. So that works because you start with a very high base and very high-quality content. But even that was not easy. So I think I think there's actually deep tech to be built in the next several years on this.

Yes. And I think that whole concept of personalized learning. I think people have a lot of different definitions of what that is and what that can look like. But -- and it still seems like we're far away, but we might be getting closer it seems like with some of the generative AI capabilities, where content could be specifically tailored to one person.

Maybe just share a quick story. So it's been about 6 months. So it took us a week to make something that looks like me and sound like me, spent 6 months trying to write code, to generate words the way that I tend to say words, and that was incredibly difficult. And maybe even after 6 months, I don't think we've got it yet. So [indiscernible] technical barrier, I think.

And maybe shifting then to -- that's kind of a good -- when you think about, as you've created courses because you've added a lot of content, created a lot of content on Coursera and through the deep learning. I guess how have you leveraged AI tools for new courses at both Coursera and DeepLearning.AI?

I feel like -- well, I personally use GenAI as a brainstorming partner. It's also a pretty good copy editor. And maybe that's what I do and what some of my collaborators do. Coursera, it turns out -- actually what I'm seeing, it actually helps a lot of people. Coursera team helps a lot of people in using GenAI for different stages of content creation pipeline. What I'm seeing is that there is no standardization. Tons of different teams are trying lots of different things. So not sure how much of this is public. Let me just say from the entire process of looking at the requirement, I want to teach this to how do you cover the service to align it with what employers are looking for and then to generate the items of the content and then come of a level of curricular to the detailed script to proofing the script. There is a lot of manual work throughout this entire pipeline, and I'm seeing lots of different people, Coursera team members, some of Coursera's partners, experimenting with tools on like pretty much all sorts of different points along the spectrum. So one of the things that I'm actually have been doing and some Coursera fellows are doing is, trying to actively stay in contact with this community, experimenting, doing very exciting work. Again, I'm excited about this. I wish I had a more crisp answer, but this very diverse experimentation and no convergence yet of the recipe of how to do this.

Yes. That makes a lot of sense. Maybe shifting then to strategic partnerships. What is Coursera doing from a strategic partnership perspective to enhance AI capabilities? How important are these partnerships to Coursera's overall kind of business objectives? What are you seeing there?

So I think it's important. I think Coursera, being an educational platform, is privileged to be able to talk to almost anyone, right? So what I'm seeing at DeepLearning.AI and Coursera is leading generative AI companies, they often welcome hope to teach people how to use their tools. And so as a neutral educational platform, we can talk to almost anyone. And this actually often gives us partners building all the leading technologies and also gives us visibility into, right, what exactly is happening on many of these businesses. So I think in terms of that, certainly, when I work with many of these companies, they will share openly with us, right, of the whatever things they release, which ones are really important, which ones don't quite work yet. And then sometimes we hear from these companies, details of unreleased products. So frankly, I actually know when some companies are going to release in these next few months because it's already running on my cell phone, under NDA so I can't share details obviously. But I think the Coursera team being connected often has I think a very good view into the next couple of steps of what might be coming in generative AI. And then with our learner base, our knowledge of learners, the data, current content assets, partner relationships this lets us brainstorm and try to innovate to create even better learning experiences. So I do think that this product innovation work to be done. But I think I'm excited about that work, and I think we're in a pretty good -- we can't guarantee that we'll call it the best product, but I think we're certainly in a very good position with the key assets to come up with -- to keep on experimenting and inventing those next products.

Well, and that's -- it kind of brings up an interesting, like you're very well connected. You get a lot of visibility. How closely are you taking the visibility that you have and what's going on in the broader AI ecosystem and trying to leverage that and try to pull that into Coursera's product? Kind of helping Coursera navigate the changes that are coming? Are you taking some of that? Even if you can't share specifics on what's going on, can you take some of that insight and help it to steer where Coursera is heading?

Yes. So I think with Jeff and Ken and our executive team's support, I have been spending quite a lot of time with some of Coursera's executives and engineering and product teams, I'm definitely working hard in trying to do what I can. Having said that, I don't want to make it too much about me. I think Coursera really has many very good engineers. Actually, one thing about Coursera, even the nontechnical people, like the whole company uses generative AI. So I don't know, I was chatting with Marni, our Chief Content Officer a few weeks ago. And then she was actually telling me how some of the stuff that she sent me, which I thought was very good. She said, "Oh, yes, I had GenAI help me write that stuff to send to you, Andrew." I was like, "Okay, that's good to know." But I think the whole Coursera team is actually -- I think certainly we've been to edtech sector way above average, in my opinion, in terms of the sophistication and familiarity with generative AI and the ability to then marry emerging AI technology with the domain expertise of education and training.

And maybe then, you kind of brought up the whole talent concept. What -- from your perspective, what is Coursera doing to attract and retain top talent when you think about especially AI talent and machine learning talent? What are you guys doing to attract talent there?

So I think many people are joining Coursera. And when I chat to the Coursera team, one thing that I -- that kind of warms my heart is how mission-oriented the team has always been and still remains to this day. And even today, when I look at a lot of society's challenges, it's not that education is the panacea, but when you wake up in the morning and think, what do I want to work on today to really move the needle for the world, there are multiple things we could do, but education is certainly very high up on the list. So one thing that really gratifies me is how much many of Coursera's team is -- has always been in it and continues to be in it for the mission. And then I think the excitement of the technical and product innovation, I feel like I know a whole bunch of people at Coursera that still go to work every day, excited to go and do that work. And maybe just to share with you one other common misconception about AI. I know that we read in the headlines about an AI engineer makes $5 million or whatever, right, and the salaries and so on, seem very high. What I'm seeing in the market is because the AI model, foundation model training layer is hypercompetitive, I think there a number of large players that all feel like they can't afford to lose in terms of training the very large AI models. There is a relatively small cohort of people that are highly skilled in training these AI models. And so the market for this layer come into AI stack, the foundation model layer, the number of people they know how to train them is relatively small. There are a number of very large companies that all feel like they can't afford to lose. And so the salaries, the engineers that know how to do that has gone through the roof. But it turns out that if you focus on the application layer, it's not that salaries are not high, but they aren't through the roof, the way that they are for the foundation model training layer. And so I think Coursera with a U.S. and global-based workforce, has tons of highly skilled engineers that do really innovative work.

That's helpful. It makes a ton of sense. I guess, I'm going to ask what about education and then maybe shift to the future in an AI world. But a lot of discussion, I think, recently about changes for the Department of Education under the new administration. You're an educator yourself. You obviously are very connected in the broader education ecosystem. How are you thinking about those changes and the potential impact to Coursera, I guess, and the general broader edtech landscape?

Yes. I think we definitely think a lot about that. And I think the next 4 years, I think there's uncertainty in what exactly what happened in the regulatory landscape in education in the United States. I think the change over the administration in the United States will probably affect global institutions of higher education less because, right, U.S. DOE primarily affects the United States. But so focusing just in the United States. One thing I hope is, the edtech sector has had many wonderful good players and then a handful of players that, frankly, did not really serve learners in the right way. So I think that -- I hope that in the future, our society is able to continue to manage the potential negative impact of bad players to keep the education ecosystem trusted and healthy. And then maybe some things I wonder about would be, this is maybe less affecting concern directly, but universities, how will -- so universities in the United States depend a lot on federal dollars for funding, both for tuition dollars at the undergrad level say, as well as research dollars for like NIH, NSF funding for research and a lot of the institutions. So depending on how the flow of funding goes up or down, this will have a very material impact on a lot of American universities. So how that changes will matter. And then because of tensions between the U.S. and China, the flow of students from China to the U.S. has also become much more challenging, and that was a meaningful source of revenue. And I think it was actually a win-win for the students and for the American universities, whether that has become more challenging. We're already seeing universities in the United States having to lay-off faculty, really tight budgets. So I think as the edtech sector continues to go through challenges, what will happen. There is one thing that Coursera has been trying to do to help universities, which is there is lots of demand for job skills relevant content to learn GenAI and so on. And frankly, many wonderful universities and wonderful faculty just do not have enough AI professors to teach the cutting-edge AI content. And in fact, if you're teaching, let's say, a psychology major. That's going to graduate going to be a marketer. Do you have a professor that can teach the psychology major that uses generative AI to set them up for success for the marketing job? One of the challenges -- and then I think as universities face more pressure to ensure high employability of their graduates, I think the university tenure system, academic governance has been difficult for all the universities to move fast enough to bring in the new types of knowledge that are needed for the workforce of the future. So one thing Coursera has done, I would say, very successfully in some geographies, it'd be exciting to keep growing this in the United States as well, would be to help augment existing on-campus full credit type of programs. So there's additional knowledge to help set up students going through academy programs for career success. And by the way, when we think about higher ed and the transformation of -- through GenAI, maybe 3 quick pills, I'll be quick. One is university operations, a lot of [indiscernible]. Second is [indiscernible], like things like Coursera Coach, AI-TA and whatever. I think Coursera has good technology there. And then finally, and the hardest one is the future of work because how do you train someone to be ready not just for the job that is available right now, which is already hard enough, but to train someone to be ready for whatever job will be there 4 years from now when they graduate. So I hope Coursera, with these assets and reach and signals, can play a meaningful role there.

Well, that hits close to -- I've got very young kids right now. I'm kind of thankful that they're not approaching that decision right now because I have no clue of what I would tell them would be the most impactful course of study as you think about moving into the higher education ecosystem to that point. You don't know necessarily what skills are going to be the most relevant. There are probably some base level skills that will always be relevant, but it's tough to predict. I think it's gotten way tougher to predict what types of roles will be in demand even 3 or 4 years out.

How old are your kids?

I've got 7, 4 and 1, just turning 1.

Wow. Can I make a suggestion?

Sure.

As they get older, have them learn to code. I think I know people wonder with GenAI, maybe no one needs to code anymore. You just tell the computer what you want. But what I'm seeing now, and I think for a long time, is people that understand coding will be able to do much more with computers than people that don't. And I think this remains true for a long time. I think one -- it turns out that when you tell a computer what to do, maybe it works some fraction of the time. But there's an important fraction of the time that it just doesn't work yet. And then if you have the deeper understanding of how computer works, you can break through just writing English or whatever language. Then people that know how to code, it's not that you need to write code all the time, but people that have that deeper understanding. And I think one of the most important skills for the future will be to be able to tell the computer exactly what you want and get the computer to do it for you. And one of the most powerful ways to do that still and now for the foreseeable future would be to really go deep and learn coding and take control of the computer and to control AI and make it work for you, no matter what job you end up in.

I love that. Yes. I will take that to heart with my kids. Maybe now shifting a little bit more to the future. And I guess you were talking about kind of the different kind of layers of the tech stack. But just as we think about tech stacks in the future, what's your view -- I think this has become a bigger debate, the view of software as a role. As I think there's a narrative out there that software could become less important over time, potentially disintermediated by AI, especially on the workflow side and the application side. But at the same time, the data that's captured, I think, within software when serving as the system of record, at least right now, is arguably becoming more important. What's your view on the role of software and how that can evolve over the next 5 to 10 years?

I think software is going to be important for a long time. Maybe I just don't see how software could go away. And AI is software and AI sits on top of software. But some exciting trends. AI is making software developers much more efficient. So I think -- I know there's a little bit of hype about this, but I think that -- but a lot of hype, I tend to say, don't buy into the hype. But I think the hype about AI making software developers more productive and more efficient don't really is true. And then as the cost of software goes down, one of the things I'm excited about is really empowering every individual to write small software programs. And I think that would be the address of a lot more applications in the long tail of software. So until recently, software has been so expensive to write that you have highly paid software engineers, a small number of them write a small number of applications and everyone uses the same application. So this is why everyone uses a handful of web search engines, a handful of whatever. And it was just completely uneconomical for, say, a mom-and-pop store to hire a software engineer to write code to customize their LCD panel display outside the window or whatever. You just don't do that. But as the cost of software development drops and maybe the operators at the mom-and-pop store can use AI to help them learn a little bit of coding, help them code, but still learn enough. Then I would love to see if a mom-and-pop store wants to build a highly custom LCD display, less important to do it. Maybe just for myself, just -- I think I have a decent machine learning engineer, but to be candid, I'm like a mediocre. Actually, I'm a s**** front-end engineer, so-so back end. But I find that with generative AI, I end up writing more programs myself. And [indiscernible] dumb parent stuff. My daughter has an obsession of bunnies. Over the Thanksgiving holiday, code up something to take a picture of her, replace the background with tons of bunnies. And so when I do these things, I can now, with generative AI, be much more productive, coding stuff up for the enjoyment of one human, my daughter. And I think this is a very narrow use case, not very economic. But I think as the cost of software development goes down, there'll be a lot more of these very long tail types of applications that now makes sense to write custom software for. But software will be important, but as this development cost comes down because of AI, I think we'll actually see a lot more of it.

Got it. Yes, that's make sense. I know we're coming close on time. maybe the regulatory framework. How do you think about regulatory frameworks and how they might need to evolve and to really to drive the right balance, I think, between innovation and protecting the broader public as we think about generative AI?

So I would love to see more governments invest more in innovation and going for the upside. I think the upside for AI will be tremendous. Many governments are thinking about protecting the public from AI harms, which is fine, too. I think we should think about that, too. But what took me by surprise over the last 1.5 years, 2 years was the intensity or the lobbying effort by usually some large companies in the frankly, false name of AI safety, to try to pass cyphing regulations to stifle open source software. So open source software is when someone writes software or builds an AI model and releases it free on the Internet for anyone to use. And it turns out that if you spend hundreds of millions of dollars, training an AI model, it's really annoying if someone releases some of the models for free because this really degrades the value of your investment. But what started to happen a couple of years ago was there was intense lobbying efforts in the U.S., in Europe, some of the places around the world, which claimed that AI is so dangerous, we must put complex regulatory burdens on it. And I think this was an attempt, among other things, to stifle open source software. And I said this in the false name of safety because AI is a general purpose technology. And I think there are AI risks, but more the application layer. So technology like AI is something that can be put into many applications. Maybe to make an analogy, I think an electric motor is a general purpose tool. You can put it into a blender, into electric vehicles, into a dialysis machine or into a smart bomb. If you ask the electric motor maker to guarantee their motor will never be used for anything dangerous, it puts them in an impossible position because they can't control how someone else uses it. But it does make sense to not look at the technology, the motor, but the applications and demand that blender manufacturers make sure blenders are safe, go to electric vehicle makers and demand EVs are safe. And I think we see the same thing with AI. The AI model is a technology. And what I saw with some of the regulatory capture attempt was to try to make AI model makers demand, know whenever you use it in an unsafe way. But that's the wrong layer to regulate it. I think if you want to put AI in a medical device, well, let's make sure your medical device is safe. If you want to put AI in a -- for political advertising, well, what's the standards, what's okay for political advertising and not. So I think regulators, by regulating the application rather than the technology, will do a better job protecting consumers about stifling innovation. Over the last year, I think we've beaten back a lot of the worst regulatory proposals, but this seems to keep on popping up. So I think we have to remain vigilant to really allow AI to have this upside. And also think about the risky application. Like one of the most disgusting applications I've ever heard of was nonconsensual deepfake porn where, for example, there have been highly inappropriate images of even minors, really affecting the mental health of some girls that were victims of this. So that's just disgusting. Really glad that the Senate passed unanimously right laws to generate liability for that. So I think we're actually doing work -- sorry, not me personally, but I think governments are doing work to get rid of the bad applications, but we also have to be vigilant to not stifle the technology innovation.

Yes. Got it. And I have this as a quick hitter. I don't think this is an actual quick hitter. How far away are we from more artificial general intelligence? What are the biggest challenges to get there? Should we, as a society, want to get there?

So AGI, artificial general intelligence. So what -- I think -- so AGI is, I think, has been most widely defined as AI that could do any intellectual tasks that the human can. So one we have AGI, we should have self-driving cars because I could do that, AGI should do that, too. So for that definition of AGI, I think we're many decades away, maybe even longer. I hope we'll get there in our lifetimes. But the biggest barrier to getting there is we just don't know how to do it. There is no road map. We will still need tech innovations to get to AGI. Having said that, there have been alternative definitions of AGI that really lower the bar. So one alternative definition has been AI that could do most economically useful work or something like that. I was chatting with one of my economist friends and he pointed out to me, he said, "Hey, Andrew. Well, that's the definition, 100 years ago, most of the United States was in agriculture. We've certainly automated most of agriculture. So if only we define AGI 100 years ago, we would have gotten there like 30 years ago." So depending on how you define AGI, we could have gotten there minus 30 years ago to maybe plus 50 years and anything in between would be reasonable. But to do any intellectual tasks that human can do, I think we're many decades away. I hope we'll get there because one of the most expensive things in the world today is intelligence because if you want to hire a highly skilled doctor to hope a medical condition or frankly send kids to a great school is really expensive today because it costs a lot to train a smart wise doctor. If we can make intelligence cheap, then it would really give everyone access such wonderful services, some of which are -- imagine if everyone today can hire an army of smart well-trained, well-informed staff to solve all sorts of problems for you. I think that will be wonderful. Today, there are some very, very wealthy people. They can hire that army of staff, everyone can hire that army of staff. I think it will be so much abundance and so democratizing. So I would love to get to AGI. I wish I knew how but I think we'll still need multiple technical breakthroughs before we know how to get there.

Got it. Well, this is the last question. We covered a lot. This is more for me. If you knew what you know now about AI and the state of the education ecosystem, would you steer Coursera in any different directions? If you thought back to when you founded it, I think it's 2011, would you do anything differently with the asset?

Gosh, with the benefit hindsight, boy, I personally made so many mistakes, there is so many things I do differently. Maybe one thing, I want to say one thing is an exciting opportunity looking forward is product innovation. I think we're in a very good position with a large user base, the trust of learners, the trust of excellent content partners to then take advantage of the new and emerging AI technology to invent or work upon us to get really brand-new learning experiences that we can then take to scale across our pretty wide platform. So one thing I'm actually really excited about. But I tend to be very impatient as a person. So part of me always wishes everything was already done. But I feel like the product innovation to invent even better experiences, that, I think there's a lot of upside to that.

Sounds good. Well, we will end it there. Thank you so much, everyone, for joining, and a huge thanks to Andrew for taking time out of his busy schedule for us today. I would highly recommend Andrew's content on Coursera. I'm currently taking the generative AI course for everyone, generative AI for everyone. So making my way through that now. I hope everyone enjoys the rest of their Wednesday and please reach out with any follow-up questions. Thanks, everyone.

Thanks, Stephen.