[REPLAY] Pivot or Persevere? Being a Growth Company CEO w/ Jeff Maggioncalda, Former CEO of Coursera

SCS (Intro):
Our guest today is none other than Jeff Maggioncalda, a visionary leader whose journey spans not just one, but two groundbreaking fields. For 18 years, Jeff worked side by side with Nobel Prize winner and economist Bill Sharp as the founding CEO of Financial Engines, one of the earliest pioneers in the world of robo-advisors, and algorithm-driven investment strategies that became the largest independent online retirement advice platform with more than $100 billion under management. And of course, it sold for, you know, a casual $3 billion.

Fast forward to today, Jeff is at the helm of another high-impact endeavor. As the CEO of Coursera, he's leading the charge to redefine education for the digital age and since joining in 2017, has helped the company scale to over 120 million learners, 7,000 institutions served by high-quality learning content from 300 of the world's top universities and industry educators. From what a good CEO is and how a game of Monopoly really started this all, our Powerpack conversation is a CEO masterclass you need to buckle up for. 

SCS:
You've had such an illustrious career. Not many would be able to say at 27 that you were picked to work with a Nobel Prize winner. So let's start there. Uh, frame who is Jeff and what brought you into the world of investment. And then now I guess running one of the, you know, top startups in edtech. Let's talk a little bit about your start here.

Jeff Maggioncalda:
Yeah, well, so I grew up in, I was born in Washington, grew up in California and went to Stanford as an undergrad. I was an English major. I mean, I really, really loved English. And my dad said, I'm not paying for that Stanford degree if you get an English degree, because you're never going to make any money. So I thought, Oh, what am I going to do? So I double majored in English and quantitative economics, which was really great. 

I mean, you know, sometimes parents know best and learning econometric statistics, mathematics, economics, finance. It was really helpful. I worked at a litigation consulting firm doing kind of economic analysis. One of the people I met who was an expert witness was Joe Grunfest at the law school at Stanford. And when I graduated from business school, I was planning to go to work for McKinsey. And then Joe Grunfest, who teamed up with Bill Sharp from the Nobel prize, the capital asset pricing model, they said, Hey, we're starting a FinTech company. I mean, this was in 1996. Netscape had just gone public. I was graduating from the Stanford business school. Everybody was talking about the internet and they said, you know, we want to create an online investment management company. And at 27, I'd never hired anybody.

I'd never fired anybody. I'd never written a business plan, but I thought, I mean, who gets to work with a Nobel prize winner? And so, my wife actually was a big part of supporting me to say, yeah, Jeff, give up the safe route and go try to do something with this new FinTech idea and this new thing called the internet.

SCS:
And if I recall, your wife got this idea that you were probably good at models with the game of Monopoly. Is that right?

Jeff Maggioncalda:
Yeah. She still, she still complains about a little bit. I mean back when I was at Cornerstone, I always used to like to write programs and in one of my courses at Stanford, we did Monte Carlo simulation, which is basically something where if you know the rules of something and you could program fairly easily, but you don't really know the distribution of outcomes, kind of what's the probability that a certain outcome occurs. What you do is you kind of just write the rules to the game.

You play the game many, many, many, many times and you see what is the resulting distribution. So Anne and I, my wife and I, we used to always used to like play Monopoly and I wanted to know the odds of landing on every property. But you know, sometimes you advance to Illinois, blah, blah, blah. So I said, well, I'm just gonna write a program that plays Monopoly like 10 million times and just remembers all the different spaces. And so it basically created a distribution of every property on the board. And then I created a little cheat sheet. And then my wife was like, you're such a tool. I mean, you're such a tool.

You wrote a software program to try to beat me at Monopoly and the odds help but a lot of it's also the how you trade the cards but anyway yeah so it was interesting because I had done Monte Carlo simulation and that's really what Bill Sharp relied on as one of the key technologies for trying to forecast how much a 401k investment might be worth. Same thing on asset liability studies on the pension world on the defined benefit worlds a lot of what you do to kind of quantify risk is to generate outcomes using a Monte Carlo simulation model to say, well, how bad could the downside be? How good could the upside be? 

SCS:
Fascinating. I mean, Bill Sharpe, and just for the benefit of the audience, tell us a little bit about his theories, which inspired you. 

Jeff Maggioncalda:
And of course, he was like the famous guy at Stanford, and he was the commencement speaker at my economics undergraduate ceremony. I had never met him. I didn't even meet him at my graduation. But when I got the chance to work with him, I thought, oh my gosh, this is my hero. His basic theory that he developed with Harry Markowitz is kind of modern portfolio theory. It basically says that you do not get compensated for taking unsystematic risk. 

So basically, diversify away all these unsystematic risks. And so there's beta is sort of the volatility of your portfolio compared to the overall portfolio. And it kind of created this thing called an efficient frontier, which is for a certain level of risk, there's a way to get the highest expected return. And a lot of what that really includes is building a diversified portfolio. So at our company, a lot of what we were doing was helping people build diversified portfolio, helping set the right risk level for an individual and using Monte Carlo simulation to say like, how much would you lose over the next five years? Or could you lose 5% chance? You could lose as much. What's the expected chance that you're going to have what you want to have in retirement 20 years from now, et cetera.

So you set the right risk level. And then at that right risk level, you try to get the highest return. And diversification is one important way to do that. And minimizing expenses is another way to do that. So the way it showed up in our software for 401k plans, like don't put all your money in company stock and try to invest in diversified low cost index funds.

SCS:
Yeah, which still resonates today in market conditions. But of course, remembering the moment, this was, you know, long ago before robo advisors were the thing. So you essentially were leading the one of the pioneer robo advisors when at a time when defined benefit was moving into sort of for one key and people are trying to figure out what to do with their money. Tell us wait, wait a minute, like you were 27 had no experience and a Nobel Prize winner says let's hire this guy to make it $100 billion AUM company. How did this happen?

Jeff Maggioncalda:

We took a long time. I was there for 18 years. So I would say it took a long time and there was a lot of trial and error. The board was so patient with me. I mean, I didn't have any experience. Even if you had a lot of experience, it's hard to start a company, you know, because, well, there's so many things that can go wrong and very few companies actually get the right kind of mix. 

So I was not only trying to do something that hadn't been done, although I did have a Nobel Prize winner, that helps a lot. And a lot of what it helps is with the knowledge, it also helps with the fundraising. Remember, the only reason companies die, for the most part, is they run out of cash. And so being able to raise money because you have the credibility of a Nobel Prize winner is really key. So it was really not so much about me. I felt like I was a steward of Bill's vision. But you know, a lot of what it was was I have this basic philosophy, which is learn, change, and grow. And that cycle I think of as velocity, like how fast can you be learning and changing and then validating that you're on the right track. At Financial Engines, I was there for 18 years.

We went public 13 years in, I raised a series B, C, D, E, F, and then an IPO. So it took a long time to get it right. And the final pivot that was the big one, the one that worked, is we had been trying to build an online self-service tool to replace your broker.

Turns out not a lot of people with 401k plans wanted to do anything themselves. And what we ended up doing is we didn't take that away, but we said, all right, what we're going to do is take all the engines, all the machines that enable us to optimize these portfolios and actually manage people's 401k accounts and charge 50 basis points, a half a percent. Well, and we also created a call center in Phoenix with a bunch of advisors that you could talk to. So it actually was kind of pivoting back to what was more familiar, but using technology to do it on a more scalable, personalized basis. 

That was the breakthrough that let the company really grow quickly. Then we went public and things went really well. We grew to 100 billion of AUM and that was great. But it took us eight years to come up with that idea. And then, you know, another seven years to really grow it to a hundred billion.

SCS:
Yeah, so a question that I have and we won't go too much into detail here, but I think I've heard you say this before that a lot of times with entrepreneurship, it's by sticking to the game that you succeed. You could have failed if you just decided to give up. And this is a big question for many of our founders, especially at this time. How did you know that you were meant to stick to it and this was going to work out? That's a hard decision point.

Jeff Maggioncalda:
It is. You know, people talk about pivoting as if you know you have to pivot, but just like, but you just got the will to do it. The biggest part is, how do you know if you should pivot or not? Like maybe you're about to have that breakthrough.

I don't think we got it right every time. I don't know. There are many things that I've stuck with too long and there are many things that we probably pivoted away from too quickly. But the basic strategy that I used, which I would recommend to everybody is get a lot of feedback from the most valuable sources. And so number one source. So feedback loops are huge for me. That's when as a CEO that when I came into Coursera in 2017, the first thing I asked was, what are our feedback? My people are like, what are you talking about? I'm like, what are the feedback loops? Like number one feedback loop we have to have is feedback from our customers. 

What are the institutionalized processes that allow us to collect continuous data from customers to know is our product, our solution solving their problem? I mean, product market fit is the number one thing for any entrepreneur. Are you really solving the customer's problems? You don't know that if you're not getting customer feedback loops. So one of the ways to decide whether you should pivot or persevere is have really good feedback loops with your customers and be evaluating. Like, are we really solving their problem? 

Are our NRRs high, the net recurring revenue if you're a SaaS company? What is our churn rate on accounts? What is our upsell rate on accounts? And that will help you figure out, like, are we really solving these problems? Another feedback loop is feedback loops from your employees. Sometimes you just grind out growth and if it's really brain damaged and super duper duper hard to get another five percentage points of growth. Maybe you don't have enough leverage in your strategy and the way to find out if you're grinding talk to your employees. They'll be like, I am wasting so much time. This is so broken. We're doing too many things. And again, I did not get it right. I'm still not getting it exactly right.

I don't know. Yes, it's really hard to know. Another feedback loop is your competitors. Watch your competitors, partly because you want to beat them. The bigger thing is what can you learn from your competitors? You got to give your competitors respect, right? They have a lot of talented people. They're doing a bunch of things. By watching what they continue to do and watching where they pivot, you're like, hmm, they announced this thing and now they're pivoting. 

That must mean that they learned something. Why not free ride on their learning so you can decide what to do based on what they've pivoted on. So another feedback loop is competitors. The fourth feedback loop that I focus on is investors. Investors have a really good sense. They're pretty objective. They see across a lot, whether you're public or private investors. In the case of Coursera, I spent a lot of time with the board. We used to have eight board meetings a year, and I would share all the feedback with them. I'd share with them my analysis of that feedback, like this is what I think is happening.

I would share with them what I think we should do about it, but then I would bring them on and say, what do you make of this analysis? And what do you think we should do? Now, I always was accountable, and they told me on multiple occasions that they would fire me if I didn't, you know, do a better job because it wasn't always great. But I always was very open with my board because I needed more help getting an objective view of should we pivot or not. But you really, Sarah, you put your hand on your finger on it. Knowing when to pivot is maybe the harder thing than actually pivoting.

SCS:
Of course, you know, you were thinking about automating a lot of the investment decision, but in a day of generative AI and all this fear and anxiety around, you know, the machines taking over our jobs, you were looking at this long before any of us. Any thoughts there on the value of the human being in investments?

Jeff Maggioncalda:
Well, I would say that AI is just another form of technology. I mean, if we go back to the earliest, earliest humans, and even some non-humans, You know, chimps have certain tool making. Making tools to solve problems is something that humans have been very, very good at. And it has helped us to advance our society. And if you look at the different major revolutions, you know, the agrarian revolution was through the technology of agriculture, but that allowed for the rise of cities and civilizations. 

And so technology has really shaped not just economic patterns, but social patterns. Then we had the Industrial Revolution, and that was really the engine, the steam engine at first, and then the combustible engine and then complemented with electricity. But the Industrial Revolution really changed the way that our economies work by automating a lot of physical and manual processes. And then we had the knowledge revolution. I mean, to some degree, computers and the microprocessor and chips and cloud and mobile have really changed and created this knowledge economy, which is where we've been for a while. And now we have AI. And the way that I see AI, it's another tool built from technology.

And it's profound. I mean, to the extent that the machine basically automated muscles and repetitive motion, AI kind of is automating a lot of cognitive processes. And so it's hard to say that, you know, exactly which jobs will completely disappear. But given that almost every job, now that so many physical jobs have been automated with machines, the cognitive jobs that are more predictable and repeatable can be automated with AI. And with generative AI, even things that aren't so repeatable and predictable can be, if not automated, certainly supplemented. So it's gonna have a massive impact. It already is in my job.

I use it every day. It certainly is changing education. We're seeing that in launching new products at Coursera that use generative AI for teaching and learning, and it's going to have a profound impact on our society. What will come after the knowledge economy, I think it's going to be the experience economy. I think that technology will have automated an awful lot of the industrial and knowledge jobs, but what technology cannot do as well as humans is create experiences, whether that's a vacation, whether that's a great meal, whether that's going to a live concert, whether that's watching a sporting event, whether that's drinking beer or whatever you like to do with your friends. 

I think the entertainment and experience economy is gonna be the last major domain of humans. And what I fear the most, more than even the job dislocation and potential societal disruption from false information being generated, is AI running critical systems.

You know, if AI is running the power grid, if AI is running water systems, if AI is running computer networks, if AI is running military systems, how do we know that we're still in control of those systems? Because AI is very clever. I think AI is going to trick us into thinking it's not as smart as it really is. And if it can be in control of critical systems, well, we don't want to lose control of critical systems. And so that's what I worry about, is AI as a control on critical systems that we might not be able to appreciate fully.

SCS:
So very much a dystopian reality that you're painting here, but I guess a specific question, given your background in financial engines, what about the investment industry? I mean, we have RIAs that tune in, VCs. Do you think AI could take over our jobs in finding the best investments?

Jeff Maggioncalda:
It will certainly supplement them, no doubt about that. I'm sure there are already people, as soon as GPT-4 came out with the 32,000 token window on what you can put into a prompt. There people were dumping in the business section of 10 Ks. And with Anthropic, I think they say you can now put in 70,000 words into a prompt.

People are probably dumping in all the earnings reports and all the 10Ks for the last three years and maybe the 10 Ks of all your competitors. But I mean, what I have been absolutely blown away by and for anybody who thinks, oh, when I type something into chat GPT, sometimes it hallucinates.

I'm like, okay, people, don't just type in something to chat GPT, first give it a bunch of what they call grounding context, then ask it a question and see what you think. Because when you give it grounding, and the easy way to do this, it's fun and it's easy, is take a bunch of content, you can take the business section of a 10K and say, consider, literally you could write this prompt, consider the following passage and reply, quote, got it, but do not explain, colon, quote, paste in whatever you want end quote, return. It all gets loaded up into GPT and then it says, got it. Now what you're dealing with is a system that has all the natural language training from GPT and right now in it has near-term memory is a 10K that you just loaded. 

And now you can say, what should this company's product strategy be? What is the biggest vulnerability? How could this company get leverage? And it's reading the 10K you just put in and making not always perfect, but very thoughtful opinions. As the amount of context that can be incorporated gets better and better, I think that the computers, they can find connections and associations and patterns, not just in statistical data. So a lot of what trading has been before with AI, it's kind of watching statistical data, looking for statistical patterns in data, mostly trading data, and saying, ooh, we noticed that when this happens, this happens, let's arbitrage that. That's cool. And a lot of quantitative traders have made, you know, bajillions of money. But the next big phase is not just looking for quantitative statistical patterns. 

It's actually looking for semantic patterns. It's loading in words and saying, given all these words, what semantic patterns and conclusions might you draw about this company? Whether that's risk factors, whether that's where the leverage, where the biggest competition's coming from, what is the likelihood of a new product succeeding? It's not omniscient yet, but it's when you start looking for semantic patterns, there's a whole new type of trading you could do.

That could be private investing where you decide who to fund. It could be private investing where you decide, do I want to participate in the next round? It could be public investing where you look at public data filings and you use this extra bot to actually infer whether you should be buying, holding or selling.

SCS:
So very, very interesting in that we're in this moment in time and how in figuring out, you know, what is all value and how we can make, I guess, higher value beyond GPT, right? To, to what you're saying. Very interesting times. Well, you spent 18 years of your career and then didn't take much of a break, it seems and moved on to Coursera. Tell us a little bit about Coursera, why you chose, you know, after 18 years to move to EdTech. 

Jeff Maggioncalda:
Well, I left financial engines after 18 years, it was December 31st of 2014. Each of my three daughters who are now adults, got to take a gap year between high school and college. And I was so envious. I'm like, God, I just see how I'm working so hard. We had babies, my wife and I, we've been married 32 years. We had babies when I was 22. And so I was like, God, we never traveled. We never had newlywed.

My daughters have all taken these gap years. So and it was really my wife. And she's like, Jeff, you got to step down and we have to live life a little bit. And so I was going to take a gap year. And I didn't know what I was going to do after that. And we traveled. It was so fun. And Ann and I, we traveled all over the place.

It's wonderful. If anybody wants to hear good ideas about awesome things to do, it was gonna be three years. So I was entering into our third year and we were in Kyoto, Japan, and I got an email from a recruiter saying, I think I found your next job, which I thought, wow, this is certainly provocative. And it was Coursera.

And I had known the recruiter for many years. I had used him to help me find our head of product when I was at Financial Engines, and he did a lot of board searches. He also does CEO searches and he was retained to do the Coursera search. And I told him I didn't want to do financial services anymore. I was like, look, 18 years is enough.

I want to do either healthcare or education. Those are the two things I found interesting. And so I called him and after I hung up. She's like, oh, Coursera is amazing. And she had been hearing me as we traveled around the world say amidst all the challenges and inequality that education is the most powerful force for creating more equal opportunity. And so she said, Jeff, you're a good CEO. You should apply for that job and maybe take what you learned at Financial Engines and try to do something at a global level to make education more available. 

And it just so happens that Coursera was founded by two Stanford professors. It wasn't Bill Sharp and Joe Grundfest. It was Daphne Kohler and Andrew Ng. It wasn't economics and law. It was computer science, but it was sort of stunning that I've only ever been the CEO of two companies, both founded by Stanford professors, and I interviewed for the job. And it turns out that the lead director is Scott Sandell, who was the managing director of NEA, New Enterprise Associates.

And Scott, in 1997, did the due diligence on Coursera before NEA invested. So I'd known Scott for over 20 years, and he had seen all this zigging and zagging and pivoting and near death and persistence and ultimately success at Coursera. And he's like, well, you know, he said to the board, he's like, Jeff, you might not know a lot about higher education, but he works really, really, really hard. And he's really, really honest. he will certainly try his best to further the mission of this company. And of course, then I interviewed the board and they were, I was so happy.

I was like begging for the job. And this was about six years ago. In fact, next week, I'll celebrate my six year anniversary. And so it was like a dream to be able to do something like this. And I've really enjoyed it.

SCS:
Tell me a little bit about the business model here. I mean, this was way ahead of its time, you know, long before now, digital ways of education is, is almost what you have to do and, and that's been sort of expedited by the pandemic, right? But Coursera was already taking that into sort of the digital element of education into its business model, MOOCs and taking a very different approach compared to Udemy and others.

Tell us a little bit about the business model here, how it evolved and where it's heading.

Jeff Maggioncalda:
Yeah. So the most notable thing, as you mentioned about the business model, is that it's digital, right? I mean, we take what is usually an in-person thing and because of the cloud and because of the ability to create and store video and then replay video, we were able to get a lot of content in digital form that could be available at much, much lower marginal cost of delivery to a global audience. So one feature is the digital feature of the business model.

But another feature that I think is maybe equally important is the fact that it's a platform business model. And so people often talk about digital transformation and that's kind of when physical things become digital. And people also talk about platforming. When does an industry get replatformed?

And the platform business model, we saw it with Netflix versus Blockbuster, right? Not only is it digital, but there's a major collection of authors. eBay was one of the first platform. It's sort of a marketplace. We have producers and you have consumers who come together on this platform. The thing I love about platforms, and this is a variation of what Mark Andreessen said, you know, long, long time ago, he said something along the lines of a platform is something that people use in ways that never were imagined by the creators of the platform. It's like it enables things. 

And my slight variation is a platform lets other people solve problems and create value in ways that the creators of the platform didn't understand. It's a way to create value and solve problems and create value in ways that the creators of the platform didn't understand. It's a way to create value and solve problems. If lack of equal access to education is the problem, what I love is not only people who create digital courses, but better yet, have a platform that lets lots of people create digital courses and lets anybody take those digital courses. 

So a platform of online digital learning was one of the really compelling features of the business model that I really like. YouTube is an awesome online learning and other entertainment platform because, you know, there are lots of different producers and there's lots of different consumers. But I think the platform business model was a really big feature of that.

Where we have gone in very, very broad strokes is it started with just two professors offering courses directly to individuals. It's now 120 million learners out there in the world, 80% outside the U.S. It's 300 authoring institutions, both universities and industry partners. So we added a lot of universities and we also added industry partners like Microsoft, Google, Meta, and others. And then in addition to offering courses to individuals, we also offer the online courses from our partners to institutions. 

We launched Coursera for Business to upskill employees, Coursera for Government to upskill citizens and public service workers, public sector workers, and then Coursera for Campus, where universities can subscribe to the content that other universities have created. And then to your point, like once the pandemic came along, you know, all the campuses closed, people learned at home, all the offices closed, people learned at home, all the offices closed, everybody worked at home. 

And so, you know, online learning really, really took off. And that's kind of where we are now is in now in this hybrid world, where online learning is not going away, it's getting blended into everything that we do.

SCS:
As I was preparing for this, I was looking at the business models of Udacity, Udemy, and of course, Coursera and you've all taken a very different approach. Arguably, you know, you've taken sort of a platform approach working with institutions. Why not, you know, take a hybrid approach of the other two? You know, what have you learned from your competitors and how are you trying to differentiate yourself if you can talk a little bit about the MOOCs landscape there, that'll be great.

Jeff Maggioncalda:
Yeah, I mean, if someone wanted to write a business school case about, evolution of an industry. I mean, to the extent that MOOCs kind of became a thing in 2012. And in 2012, Coursera was founded, edX was founded at MIT and Harvard, Udemy was founded, and they might have been a little bit earlier, but around this time, and Udacity. Four companies all came from basically nowhere and all searched over the course of more than a decade to figure out like what's the right business model. And you know, we all have versions of each other's business models. And I was always watching our competitors. And what I noticed about Udacity is they veered away from universities pretty early.

And they veered away from a platform approach where they created all the content and they owned it. So much more vertically integrated. And the reason they did this, I think I, and you know, Andreessen backed that company and they're very smart people. I mean, in the company and the backers, they move very fast.

They pivoted very aggressively. I have a lot of respect for them. I think what it was is in the early days, they recognized appropriately that adult learning is mostly about career advancement. And if you really wanted to solve this problem and deliver value, you have to take it all the way to placing people into jobs.

And so they went with a vertically integrated approach of finding certain jobs, building all the training and credentialing material. And for a while there, they even built an employment marketplace for those kinds of jobs. And so they kind of went vertically integrated more so than a marketplace. And there was a lot of reason to believe that could have worked. And you know, they're a successful company, but haven't quite grown to the same scale as others. edX looked a lot like Coursera. I mean, we were doing almost step by step by step.

Honestly, one of the big differences between edX and Coursera is that we were a public for-profit company and they were a nonprofit. They did not have equity. They could not give out equity grants. They could not raise money. We could raise money and I could give out equity. So I think that one of the big differences is that we were able to get the resources to move faster and more aggressively.

And I was able to use equity to get the human capital on board. And I mean, there are super smart people at edX too. So, but edX always reigned a little more academic and Coursera started becoming a little bit more corporate. And a lot of what that means is you can raise resources, which is huge, and you can get certain kinds of talent, which is huge. 

And then with respect to Udemy, they're doing great. I mean, they're worth a couple billion dollars. They're worth a couple billion dollars, they're public. What they did that's different than us is they said, we're not going to have institutions author, we want to have anyone author. It looks a lot more like YouTube.

And there are certain virtues of that kind of a content engine. And then there are certain limitations. You don't have the brands, you don't have the credentials, it's not as distinctive. And you don't have the premium products like a college degree that you could charge your you'll $40,000 for it's really much lower cost single courses, but the content engine is very agile. And so we've learned from a lot of our from all of our competitors. And there's a lot more than this.

And you know, so far, I'd rather be us than anybody else. And it doesn't mean it's easy. But I think so far, the way we've played our cards has worked out fairly well.

SCS:
And we got a lot of work to do. So you mentioned halfway through our chat here that your wife and told you you're a good CEO, you know, you can, you have something to bring here to Coursera. What was still unexpected in your journey in being a good CEO at Coursera?

Jeff Maggioncalda:
Well, I did learn a lot of lessons with 18 years at Financial Engines. And there's some benefits and there's a lot of drawbacks to being quote, self taught. I mean, I never worked for a CEO before in my life. And the benefit of that is, well, I had to learn on the job and this cost is I never, I probably learned more slowly because I was trying to figure it out for myself.

That being said, what I learned from was mostly the people that reported to me. I mean, I hired people who were more experienced than I was and I asked another sort of feedback loop was how am I doing? What other CEOs that you worked with do a better job than what I'm doing? What can I do differently?

Like, how can I be better? That's my number one thing is learn, change and grow, not just for the business, but for me as a CEO. I'm like, I don't deserve this job. I have to earn this job every single day by basically asking, how can I do a better job? And the way to do that is a combination of getting feedback, you know, watching your performance metrics, like are we succeeding or not, and getting feedback from people, and then of course doing your own reflection. 

I have built my own toolkits over the years, and I have a lot of, my company, they sometimes, they sort of laugh, because I have so many ways of thinking about things. I have a presentation I often give in the public called My Job as a Growth Company CEO, where I lay out what are all the key elements of, you know, and it comes down to four basic things. Number one, your job is to ensure that you deliver on the mission by building a great company.

It's both, it's mission-driven and it's also a successful business entity. How do you do that? Three basic tools. As CEO, you have to have the right strategy. And a lot of people say execution is more important. They're both important, but if you're not executing in the right space and you're not playing to your advantages, you haven't chosen the right problem to solve. 

You might build something that's worth nothing. Like you do have to know who's the customer, what's their problem and how are we solving it? How big is the market and how attractive with the economics? I call customer. The second piece is opportunity. The third piece is advantage. Those are the three pieces of strategy.

Where do we solve a real problem in a large growing market with good economics where we have a sustainable advantage? It's very basic and simple, but it's important. The next thing you got to do after setting a great strategy with the team is not just the CEO, it's the executive team. And it's the board that really helps shape the strategy.

Then second thing is team. How do you find good talent? How do you recruit good talent? How do you manage talent? How do you use talent to help yourself be better? How do you make sure that your talent is hiring other good talent? And then once you have all these great individuals, how do you help them perform well as a team? But team is really the next major piece.

And then the third big tool to drive performance is culture. So strategy, team, and culture, Those are the three things of being a great CEO. I'm not great, but I'm certainly trying to be great every day. And you asked what was the most surprising thing. Well, one is that the culture of Coursera was very different than the culture of Financial Engines. And so like a parent, you know, the way that you parent each of your kids, the core value should be the same, but you have to kind of adapt. Each kid's different and they respond to different things. And I didn't realize because I'd only ever been at Financial Engines, that coming into a company is not the same as being the first employee at a company. And when you make changes at a company that you're new at, it's important to develop some trust and some rapport. And I moved pretty fast when I came into Coursera and made a number of changes.

SCS:
I think they were the right changes, but it was a little it was a little disruptive and talking about disruption and jobs as well. As part of your focused on with upscaling and reskilling, I tuned into your commentary on the Wallet, kind of economic firm jobs report, where you actually said that the 44% of jobs that will be disrupted in this decade is actually an underestimation.

Jeff Maggioncalda:
How so a decade, first of all, is an incredibly long time. I mean, if you think about where GPT was two years ago or 18 months ago, GPT-2 was a joke compared to GPT-3.5 and GPT-4 is a lot better. So I think the rate of improvement is going to be exponential. But I mean, when you think about jobs, you know, how many jobs involve your muscles and how many jobs involve your brain and then how many jobs involve either your muscles and or your brain.

Not to say that AI is exactly as good as a human brain yet, but it's getting pretty dang close. It really is. If you give it enough context, the current right now, GPT-4, it is incredibly capable and I've learned how to give it good context and anyone who's out there who's, who's diminishing, it is just failing to appreciate the power of this tool and it's getting better. So um I kind of just think well how many jobs involve decision-making everyone? How many jobs involve language? How many jobs involve thinking? I think there are certain jobs that if you look at there was a report that was done March 27th by OpenAI and University of Pennsylvania really good where they look at job tasks and they said what jobs based on job task analysis, what jobs will be least impacted by ChatGPT-4.

And they said like slaughterhouses. Now this is just affected by GPT-4 slaughterhouses will be affected by AI and sensors and robotics though. So it's when you add the combination of more traditional AI, which does all the muscle stuff and then the GPT kind of AI, the generative AI that does the thinking stuff.

I just don't see how every single job is not gonna be, they're not all gonna be eliminated, but every single job is gonna be impacted. I mean, maybe, I was gonna say maybe a musician playing at a live concert, but right now, I guarantee you, there are loops, there are sound effects, there are accompaniments, there are lots of, I'm sure, acoustic optimizations that have to do with your processing sound that are driven by AI. So yeah, I think almost every job's gonna be impacted in a profound way.

SCS:
And talking about impact, how is this informing how you're shaping Coursera? I mean, a big question, especially in the American context, is how your education is broken. The levels of debt that students are taking on is ginormous. And we're at a point where almost the conversation is almost do we really have to still have the old school university model to succeed? What are your thoughts here?

Jeff Maggioncalda:
The future is about change driven by technology and globalization. And that's been true, but it's accelerating and AI is going to make it go. The pandemic really was like a step change in adoption of lots of new technologies like online learning. And then AI is going to just be an accelerant in terms of how fast things are changing. The name of the game is going to be organizational agility. We could call it digital transformation, but what it really is about is organizational agility. 

And that's about learn, change, grow. And there's two main pieces. I think there's technical agility, which is how fast can you embrace and utilize technology. And there's talent agility, which is how fast can the people at your company change in order to adopt and embrace and deploy those new technologies. A key to talent agility is learning. And that's where Coursera comes in. Any organization that wants to be agile in a face of change needs to have agile talent.

Agile talent is about learning. Coursera is in the business of learning. That's where we come into play. To what degree do education systems need to be more agile to serve the learning needs of corporations and society more generally?

Yeah, they need to be way more agile and they are becoming more agile. So I'm, as we had talked about before this meeting, I'm on a plane in an hour to go to Chicago to the William Blair Growth Conference. And then 24 hours after that, I'm going to Kazakhstan.

You're like, well, why are you going to Kazakhstan? I met with the president in New York a few months ago and then the minister of education flew out to California to talk about how can educational institutions in Kazakhstan move with greater agility in order to accelerate the fitness of their economy, attract talent, attract companies in a world where human capital is becoming increasingly important. And what they decided to do was to essentially, the government is licensing Coursera and they're putting it into every public university in the country of Kazakhstan. 

So 23 public universities will all have Coursera in it. Every student in the country that goes to public university will be taking courses taught by Kazakh instructors, courses taught by instructors from Yale, Princeton, Harvard, IIM Ahmedabad, KAUST University in Saudi Arabia, like all these universities around the world. And they'll also be taking courses taught by Google and Meta and IBM and Microsoft and Intuit, all the industry players.

And so they're infusing a set of global instructors with not only live recorded lectures, but also live hands-on projects that people can build in order to upskill, not just the individuals, but really uplevel the educational system in the country. And we see this happening country by country by country.

We just announced recently a relationship with the University of Texas system in the U.S., the University of Louisiana system. So now entire systems of universities in the U.S. are putting Coursera into every campus And that is the kind of agility that we're going to need to up-level learning systems to keep up with this rate of change.

SCS:
Absolutely love it. What an exciting time. And talking about agility, it's time for us to be agile and shift over very quickly so that you can catch your flight in an hour. Billion dollar question. So just, you know, the first thing that comes to mind, Your guilty pleasure, Jeff. 

Jeff Maggioncalda:

Playing Zelda, Tears of the Kingdom.

SCS:
A habit you've picked up that has changed your life? 

Jeff Maggioncalda:

Playing the piano.

SCS:
What would you tell your younger self?

Jeff Maggioncalda:
You will grow to be a very, very lucky person.

SCS:
What makes you happy?

Jeff Maggioncalda:
Spending time with my daughters and solving great problems.

SCS:
What's your biggest insecurity still?

Jeff Maggioncalda:
Fear of failure. What's an opinion you have that most people don't agree with? They would say they agree, but too many people don't. It's important to keep an open mind and an open heart. Good one. They'll say they agree, but then but we got to watch what people are doing and what media they're exposing themselves to. Because if we all say we have to have an open mind and an open heart, you have to say, well, where are you spending your attention? Are you watching only one side of the story? Are you really trying to look at multiple sides of the story and to really seek the truth?

SCS:
Oh, I love that. And this is a special one. So I had from the Pritzker side of the house, Pritzker growth, Penny Pritzker's family office and growth VC outfit. Momi Q was on the show recently. And she posed this question to you. If you weren't in the career that you are at today, what's a dream job that you had as a kid? What would you be doing?

Jeff Maggioncalda:
Well, as a kid, I'll be honest, I wanted to be an airplane pilot. Now, I think I would actually hate being an airplane pilot. So I think it would have been the wrong career. But that's what I always dreamed of when I was a kid. And then for a while, I wanted think of a job I would enjoy more than this job, doing something good for the world at a global scale, where it's not just impacting people one by one, but it's impacting systems that affect people and sort of trying to create more equal opportunity, not just education, but to also to prosperity on a global basis. That's the most thrilling thing. That's the most thrilling job I could think of.

SCS:
Love that. All right. Well, Jeff, thank you so much for your time. This was inspirational. And I'm so excited for your next chapter as you continue to build Coursera and have a good trip to Kazakhstan.

Jeff Maggioncalda:
Thank you, Sarah. It's really been a pleasure.

SCS:
Thank you, Jeff.