Nina Gené: Venture Philanthropy, Jasmine Social Investments, Impact investing | Podcast

Nina Gené is CEO of Jasmine Social Investments. Nina leads Jasmine’s investment strategy and diligence process, guiding the team to identify and support the next generation of great social entrepreneurs. Jasmine funds high-performing social ventures and outstanding social entrepreneurs who are solving a basic need of the very poor.

Ben and Nina discuss what venture philanthropy means and the Jasmine strategy on philanthropy.

We delve  into the investment process that Jasmine uses. How Nina identifies opportunities, the type of qualities Nina looks for in a social entrepreneur and an organisation.

We discuss success investment examples, how we might think of impact investing and how it may differ from grants. We talk about the advantages of being neutral to structure, ie, being able to fund using grants, debt or equity. Whatever suits. 

We chat about the influence of venture investing and how entrepreneurs think. How Jasmine shares information and due diligence and what help they give investee companies.

We talk about measuring impact, and the challenges of scaling up.

We mentioned the pros and cons of working in New Zealand, whether Spanish food is under rated and finish on advice Nina has.

Nina on the importance of the ability to scale:

“I'd say that scale is one of the most important criteria that we have because we want to make bets on people that will end up figuring it out and have a survey that will save lives. When this happens, we obviously want this to go to millions and millions of people; so that's kind of the hope and dream of it. The way we define scale we define it as an intervention that can reach up to 1 million people. It doesn't necessarily need to be multi-country. We work with an organization called Luala that are influencing the way that health is provided to a million people in one district in Kenya. That's very important and we support those groups during the R&D phase.

But what we do expect then is to scale the work only when they have that strong evidence on hand, but also the right economics of that impact. We support them through that journey and fund them as long as they show us success every year. That's why having a set of metrics and scorecards and milestones-- We're not sticklers for, "Oh, you said you were going to do ten and you've only done nine. You're out the door." We understand that there are ups and downs and we're very long term funders.”

Video with captions is available here. You can listen above or wherever you get podcasts. Transcript follows below.

PODCAST INFO

Transcript (only lightly edited)

Ben 

Hey everyone. I'm super excited to be speaking to Nina Gene. Nina is CEO of Jasmine Social Investments, a private foundation in New Zealand that funds high performance social ventures solving big problems in the poorest geographies. Nina, welcome.

Nina 

Thank you, Ben. I'm excited to see where this conversation goes and also having the opportunity to share what we've been up to lately. But let me first echo Jerome's sentiment in your last podcast about the incredible list of guests that you have assembled and how humble I am to be among them.

Ben (00:00:37):

Oh, thank you very much. The work you do is all super great, and I think this kind of venture investing or philanthropy investing is super interesting. So I'm going to roll up the first two questions together, which would be tell us about Jasmine and what does venture philanthropy mean to you?

Nina (00:00:59):

Sure, of course. So Jasmine is a private foundation based in New Zealand, and we have been doing this type of work for more than 15 years. The foundation's goal is to fund scalable impact, and what this means is we look for organizations that provide basic needs services to families living in poverty. An example would be a community health program that brings primary care to a rural village in Liberia. Where in the past, patients had to endure a walk of 10 hours to the nearest clinic, and today you have a health worker in the village that is diagnosing and treating children at the doorstep of their homes. So we have decided to execute on this vision by building a portfolio, as you've mentioned, high performing social ventures that are fundable by us and others. So we search the earth for the best operators we can find.

We add these individuals when they're designing high impact interventions and they're early in the journey. And once the model has been iterated several times and tested and bulletproof, we expect them to scale those in need to thousands, if not millions of people. So Jasmine mean is currently supporting 25 of them working in Sub-Saharan Africa and South Asia. We've learned through the years that the kind of impact that we want to see, for example, a child reaching the fifth anniversary usually requires what we call a permanent donor subsidy. So that means that philanthropy needs to step in. And this is why the bulk of our effort is providing grants to nonprofit. Nonetheless, 20% of them are for profit structures which kind of gets me to your second question about, what does venture philanthropy means to Jasmine.

So we work for a philanthropists; his name is Sam Morgan, who has been on both sides of the table. First was an entrepreneur funding what became New Zealand's largest online auction, and today he's an investor and advisor to several tech startups. So the one commonality that most foundations have, other than the corporate ones is that we get to work with individuals who are very successful in their own right. The question really becomes, "How can we use our best asset, our principles to design a philanthropic arm that obviously represents what they want to do, but also takes advantage of who they are and keeps them engaged along the way?" In our case, the answer was drawing from best practices in the two fields that Sam is an expert on; entrepreneurship and venture capital. So I would say that on the entrepreneurial side with black people, we're not looking for projects or have a priority geography list, et cetera.

The school foundation was a source of inspiration in the early days when they created a forum in Oxford to celebrate social entrepreneurs. Back in 2004, the idea of social entrepreneurship was new. So our interpretation of that is to favor entrepreneurs going to social entrepreneurship rather than social workers wanting to become one. Then last on the investing side, we apply the same framework we would to identify and fund software companies. So are they managing by numbers? Are there financial disciplines on governance, hiring and retaining talent? Then we perform really high quality due diligence and then we support our investee the best way we can. Sometimes when you back talent that means give them money and leave them alone.

Ben (00:04:45):

That sounds excellent. And picking up on that. So one of the concepts in early stage, particularly venture, is that the venture company often gives help or supports investees to some extent. Sometimes, like you say, it's just money and you leave them alone. But often, it's giving them kind of other type of help; access to networks, ideas, business models, and that type of thing. So I guess in the form of venture philanthropy, do you still give help and support investee companies in the same way? How is giving help to support investees done for you?

Nina (00:05:20):

Yeah, absolutely. And because we do work with people really early in the journey, it's a lot of fun to grow with them. So I started my career in investment banks reading a lot of stock reports-- as I'm sure you do once in a while, your day job. So I was really intrigued about the idea of translating this very published report that is shared among investors and gives you a really good overview of the management, the strategy going forward. But also an important snapshot of the relevant metrics and the financials that you need to pay attention. This is one of the work that we do with our grantees. We have this thing called the Jasmine Scorecard, where we help them understand metrics and how to slice and dice some of their financials and how can you translate it over time.

So the good news is in the social sector information has always been available to everyone through the website. You can find the quarterlies and the tax returns and the audited financials and anything you want. So our role at Jasmine was twofold. One was to get grantees didi ready. This means showing nonprofits what a professional data room should look like. The other one was writing a comprehensive analysis that has three different purposes. The first and foremost is drive our internal decisions, and we make them on an annual basis, which means that we put our investee through this process every year. The second is we would share this information with them. They get to see when it's 95% there and they can fact check or argue some points that we've made. This helps the investee and their boards to look at the work through a different lens. Everyone finds it incredibly refreshing.

But the third one is the most important, I think, because it elevates the conversation with other donors. I mean, we do this kind of work for Sam, it'd be silly if we wouldn't share it for other ones to use as an abuse as they want. So what we hear again and again is that someone would have a 60 minute pitch with a potential funder. And normally, this conversation starts with, "Oh, can you tell me what you do?" But on the other hand, many of them start with, "Oh, we just read the Jasmine report. Can we get on, on some questions that we have?" And that's fantastic because at the end of the day, these organizations only have 60 minutes.

So this last piece someone is sharing, which is exactly what the banks do with their research and have done for decades, was actually an innovation of its own when we started 15 years ago. So it has been exciting. We put our work out there and we shared with lots of people and encourage others. It's great to see a lot of funders today being comfortable with sharing their internal documents. As I always think about it, we are in the business of advancing human dignity and that means why would you not share your diligence? We should. So we disseminate our analysis twice a year. There's someone in our family office that keeps this list and writes everyone personalized emails. We have a distribution list of more than a hundred funders.

There are all types of sizes and shapes, and some of the ones that come back with curiosity and arguments about things that they didn't agree are some of the smaller foundations that really know their grantees well. So the least includes family foundations like us; new philanthropies. If you saw your business recently, probably an investment banker would've done one on you. So it's very relatable when you've kind of gone from this commercial to this social and then you come across a report that you can totally relate to. They help analysts in places like U-S-A-I-D. So it has been exciting to see the Jasmine reports we passed around. When people ask, "Oh, is it okay if I pass it?" I'm like, "Yeah, yeah, yeah. You should just move it on." Then also this theme has a common good. And to be honest, if I look back at when I started this job, when we thought what Sam wanted to do with this foundation, we never thought this was going to happen. So it has been a nice surprise.

Ben (00:10:23):

Yeah, it's excellent. So I've read a few reports and they are very much like-- and what we call in our side of the world, a kind of sell side investment report. And bringing that kind of discipline to thinking about the charitable sector, I think is really refreshing. And like you say, it's really helpful for all of the stakeholders involved. I guess as a follow on, on that, how do you identify opportunities when you're thinking about what to write about or what to investigate? When you identify opportunities, are there any particular criteria you use or how you think about themes? We've already touched on a couple about this kind of ability to scale. You are also very interested essentially in kind of deep poverty and things like that. But I'd be interested in a little bit more detail on the kind of opportunity piece and thinking about criteria or themes.

Nina (00:11:13):

Yeah. Well, first, thank you for reading them and I hope they stacked up to your many reports that you read. So through this kind of sharing and caring is we have joined the global conversation from New Zealand, which is an enormous task given that we normally sleep when everyone else is awake. So through these years, we build an ecosystem of funders, and they're our main source of ideas. So I just want to make sure you know and understand that we're not just a team in the middle of the South Pacific with a spreadsheet saying yes and no. We don't do that. I mean, we do sometimes do that. But we also get a chance to travel in country. We did an incredible trip in West Africa, Liberia, and Senegal this last spring.

This is a way to stay connected to the people we serve and find new ideas. But we also close to a lot of the people that are working with the next generation of social entrepreneurs through fellowships, incubators, and consultants. Now, most recently we have been really thinking internally at finding ways to find those extraordinary individuals in pockets of the world that our traditional networks need to reach. We have a handful of STEM for MBAs in our portfolio and really love them. So if you're listening and you are one, please continue to apply. But if that's your target, your pool is really limited. So how can we adjust the way we assess opportunities or we look at new ideas to attract different type of candidates? And remember that time is our biggest enemy. I mean, we scroll through 300, 400 ideas; not all of them with the same intensity and we're a small team.

So we had to expand the reach of the funnel with men doubling down on the team and having multiple conversations. I'm really proud to say that the last five or six editions are enterprises led by Nigerians, Malawians, and Indians. This wasn't the case a few years ago. So that's something that the team is really proud of. Then I guess last on identifying is there are the best leads happening in the most random conversations. Like the one that you and I had where you've just given me a couple of good ones. So you're always on the look for great people. You just need to allow that kind of serendipity.

Now, back to your question on criteria and themes. Well, on the criteria side, we have used the same criteria for the last 15 years, which is kind of a rare phenomenon of its own. So we talked about the basic needs, which encompasses primary healthcare and education. We do a bit of early childhood development, livelihoods, climate resilience; the entrepreneurial spirit also that we discussed, and making bets with early stage organizations that have some evidence of impact. But we like scalable models which means that, that implies that they have a cost structure that keeps going down and eventually the governments can absorb because when you're in the basic needs area, you need to scale through the public sector. We also stick to this 80/20 rule where the majority of your work falls under that criteria. That's just this five criteria that I've mentioned. As a small productive team, we really need to be on focus. But the 20% allows you to experiment with models that may challenge that core criteria.

But in our case, what we found is while the exceptions have been fantastic and really enjoyable, they've also solidified the fact that our original criteria was fine. So the last dimension that we look for is the right fit. It's really integral to the selection process because we have a very diligence heavy process and it really needs to be productive on both ends. So the feed becomes very evident in the first conversation that we have with people. Unfortunately, being based in New Zealand, Zoom has been for many years our way to talk to people. You talk to someone and you can be able to articulate the clear vision and think numerically and then you're like, "Fantastic."

In terms of themes-- and this is an interesting conversation because I think giving sometimes can be incredibly seasonal and I don't understand why. But I see money chasing specific trends and then they change the strategy and they get onto a different path. It's a non-intentional behavior of funders, but it's kind of a harmful practice for those operators that are in the trend or outside of the trend suddenly. Also, the problems that we're trying to solve are here to stay. So when you think about the importance of the first thousand days of a child's development, that was important 50 years ago, but will continue to be a hundred years from now. So Jasmine was established at the end of 2006, inspired by the United Nations Millennium Development goals. In 2015, those became a set of very ambitious 17 social development goals. So the SDGs and the SALT really provide this excellent framework. So when you kind of look at the SDG and our criteria, we come up with a good set of investment principles

Ben (00:17:18):

That makes a lot of sense. So a couple of things I get from that is when you're thinking about opportunities for investments in the future, quite a lot of them are the same opportunities as we've had in the past 15 years because these primary needs of humans, education, healthcare what is now wrapped up in their SDGs remain the same. I think that's quite interesting. The other thing on the criteria that you are using, that you are thinking about; so scale, impact, and we can maybe talk about some of the measurement things. But you touched on this and we've been discussing on and off the kind of founder, the social entrepreneur. So I guess this is a question which doesn't have any answers because if we really had this, it would be really easy.

But I was wondering what are the qualities that perhaps you're looking within a social entrepreneur that really kind of stand out? I'll mention a couple that you've already spoken about. So the ability to articulate a vision ideally simply, analytical skills. So if you have a vision but you can't talk about it in terms of some whatever you are talking about is obviously important. And perhaps a tilt to people who understand that and want to move--they're essentially entrepreneurs and they want to do something socially. But is there anything else that you would point to and do you perhaps have a favorite question or two or quality or two which makes you enable to kind of unpick this particularly over a kind of Zoom type thing about, "This is the kind of qualities in a social entrepreneur CEO that you're looking for?"

Nina (00:18:53):

Yes. And obviously, we work with people that are serial entrepreneurs and obviously, they've done it. One of them sold the first company at 25 and another one built a hundred million dollar company and then decided to do this. So obviously, it's easy art because they've done it before. But we also work with entrepreneurs in the making. It's kind of that curiosity and big ambition and this internal interest in getting to this solution and sometimes a very simplified way to understand what needs to happen in the next 12 months. Like, "The next 12 months I am going to de-risk my model, which means I'm going to do A, B, and, C. A may happen and B won't so we'll be able to pivot really quickly." But they're able to focus and de-risk the model in a very systematic kind of way.

Ben (00:20:04):

Yeah, I agree.

Nina (00:20:05):

I'm not an entrepreneur so... I can see there's a bit of pattern of recognition because I've worked with both of them, but I'm always fascinated when they explain the reasoning and how simple it looks because I just wouldn't be able to do that.

Ben (00:20:21):

I agree. And often they'll say, "By July, I think I'll have done A, B, C." Then you check in and, "Yes, I did A, I did B, but I didn't do C either because of reason X or because I thought reason Z. And now I'm going to do D, E, and F." So there's this focus and operational side as well as the fact that it is adhering to some kind of vision as well.

Nina (00:20:44):

Ben, also, it's obviously very hard to build a big business in the Bay area. But the people that we work with, they work in very unstable conditions; difficult governments, transitions, and poverty. It is so complex and has so many different layers. I think you have to be a very good and very methodical entrepreneur and have all these business principles of that kind of what we talked about; the numbers and the financials and be moving along. But at the same time, with social entrepreneurs, you need to know and be comfortable with Cals.

Ben (00:21:34):

Yeah. That's great. You have to-- I guess a lot more flexibility in how you work using the local environment.

Nina (00:21:44):

Yeah. I mean, you're going to be navigating through a lot of ups and downs and you need to adjust to it. So should we as funders really understand that there is an up and down and have more of a long-term view.

Ben (00:22:00):

Great. I think a lot of people would probably say this as a form of impact investing, but it's kind of quite broadly defined. I'm not sure you yourselves view this. Obviously, you are making impact, but you kind of use this idea of venture more. I wonder whether you do view yourself as impact investing and maybe what's most misunderstood about that or not. I guess one of the elements of investing in this way with impact is this idea that you kind of measure and track what you are doing. You might have some other-- In profit organizations it's easier because you're kind of tracking profit at the end and other metrics which go into that. In nonprofits, it can be harder. Some of your KPIs actually can be quite clear; death in childbirth and things like that are quite clear metrics, but others are not. So I'm kind of interested in about how your understanding of impact investing is and where you might fit in. Also, to what extent can you measure and track these items and how important is it for you?

Nina (00:23:03):

It's an interesting conversation because we definitely fit in the impact investing category-- not always because we're grant makers as well. I don't think we would consider ourselves as impact investor-- actually, probably wouldn't. So the main difference for those in the audience less familiar with the term, is that you can deploy money first as a grant; some money out the door. Or second as an impact investment, which means debt or equity. So you would give someone a loan they need to repay or you would become an investor in a company.

Now, the first misunderstanding about impact investing is the second world; that is the impact. Investors quickly fall into that argument of, "Oh, I'm going to lower my return expectation for a bit of impact." Impact is not always fractional. Ideally, you don't want students to be partially numerate or illiterate. You want them to finish primary school being functionally literate and numerate-- at the bare minimum. Another misunderstanding is that quantifying impact is just too hard. But when you are a commercial-- If you were an investor in Uber, you wouldn't be saying like saying, "Maybe they should be a bit less profitable because convenient rides are such a social need," or, "Hey, can we stop asking management about the financial accounts?" This shouldn't be in the impact investing. So in terms of our strategy, we are incredibly opportunistic on impact investing. So adjustment, we make impact investments only when there is a commercial model that has the potential to create real impact and we don't compromise on that, which means that there's a profitable enterprise behind.

An interesting way to look at it is you look at the cost that is required to achieve that tangible result. For example, you're helping a farmer increase their yields so they can feed the family year round. Sometimes you can cover the cost of serving that client with the revenues you collect, but other times you just need that philanthropic cushion. That's what we call the subsidy. So when we're talking to organizations-- and because we do talk to a lot of people that are really early in the days, we have that kind of subsidy discussion on what they think, "Is it a permanent donor subsidy or do you think you have a profitable model?" But we have always been about group first and vehicle after, and that allows us to look for the talent and then decide what is the most appropriate type of capital that they need. We're very fortunate that the legal framework in New Zealand provides us with the flexibility to operate this way. We're less limited than our friends in Australia or the US. So in total, I'd say that we have deployed-- 80% of the fund have been grants and 20% in impact investing. And most recently, we're doing a lot of work on the debt space because there's very few of us that can do low cost debt and both nonprofits and for-profit have this working capital requirements. So it has been a bit of fun to play a little bit of a catalytical role in that space.

Ben (00:26:36):

And actually, that might be increasingly important if the environment of higher interest rates remain. So essentially, what we'd say in the business is the cost of capital or essentially the cost of debt is rising. So ability to access cheaper debt on whatever terms might be quite interesting. It does strike me that one of the advantages of your model is to be able to be structure neutral. If grant making seems to be the way forward and however you need to structure that grant, then you can do it. If it's some other type of way of doing it then that will be there too. But it also strikes me even within your grant making, you are tracking some forms of measurement. It's just not, "Oh, give some money away. We'll check in with you in a year and let's hope that everyone's happy." You've got that rigor which has come through.

I think that has been a sort of, I guess-- In the last couple of decades, philanthropy has got more on board with that. But it still seems to be a relatively young idea that actually, even if you are going to give away grants, there is some way or there should be some onus on the people involved to measure and track how effective that is because you're going to have opportunity cost and all of those types of things. So that strikes me as do you think the bottleneck for your sort of either investing or grant making is on the funding side or do you see that there's other hurdles, sort of ideas or regulatory, or just the coordination problem or things set up? Where do you see the bottleneck or the challenges, I guess in philanthropy or in venture philanthropy at the moment?

Nina (00:28:15):

We should get back on the measurement because I think it's an important part of who we are and what we do. We do believe that you need to be able to quantify impact. So in terms of funding of bottlenecks, there is always room for improvement on the funder side, I must say, because I'm part of that group. I joined Heather Grady on her team at the Rockefeller Philanthropy Advisors on an initiative that I think is very important called the Shifting Systems. And that's about encouraging funders to become long-term and restricted funders like us, but also be more savvy around the diligence they do and how they collaborate with others.

We're not perfect donors. We're constantly reflecting on how we can do better. "Did we use all the documents we requested? Are we asking for the right questions in the right way? And how we add-- as we've discussed-- more value outside of the dollars we provide," which the answer is always the same. "Do short to the point diligence and connect us to more money." But on the supply side, we have been given a lot of thought about how can Jasmine play a role in helping other social ventures bring that strategic thinking early on the piece. That's what we have been doing with our grantees. And is there a toolkit maybe that we can put on our website that provides groups outside of our portfolio because we're only going to be able to work with 30, 40, or 50, let's say. But how can we have the next 500?

And obviously, it involves the theory of change and designing for impact and mapping behaviors changes. But increasingly, it incorporates that idea about the financials and how important they are and forecasting three or five years ahead, which is a very unnatural process for nonprofits. It's not because they don't think it's important, it's because they need to raise money every single year. So if you forecast five years ahead but then you need to explain every single year that you need money whether you met those expectations, it just doesn't... And I understand it's conflicting interest. But anyway, understanding the economics of your business and what's today is important. So I think there's this bottlenecks both on the door and donor side, as, as we call it. And we have been trying to think more broadly on how we can help.

Ben (00:31:15):

That makes a lot of sense and it's pretty interesting. I was going to pick up maybe on the measurement piece again, if you wanted to make some comments. Maybe interlink this with-- I do think one of the slightly unusual aspects of what you guys do is this emphasis on the ability to scale, or at least it being an element, which is actually very typical in venture capital in general-- actually, less usual as companies grow really big. So big public equities you might be interested in growth, but they're already so big they are at scale, essentially. They may be scaling other things. So I'd be interested to know how important is it when you look at things about an organization's ability to scale. Is there something that you can kind of measure, or is that measurement part of it? And I guess particularly when you are looking at entrepreneurs in the making or businesses which are essentially at that seed or that quite early stage, the ability to know whether they're going to scale or not is often quite uncertain, I guess. So I'd be interested in how you're thinking about an organization's ability to scale, how important it is and what you are looking for in terms of that.

Nina (00:32:24):

I'd say that scale is the most important criteria that we have because we want to make bets on people that will end up figuring it out and have a survey that will save lives. When this happens, we obviously want this to go to millions and millions of people; so that's kind of the hope and dream of it. The way we define scale we define it as an intervention that can reach up to 1 million people. It doesn't necessarily need to be multi-country. We work with an organization called Luala that are influencing the way that health is provided to a million people in one district in Kenya. That's very important and we support those groups during the R&D phase.

But what we do expect then is to scale the work only when they have that strong evidence on hand, but also the right economics of that impact. We support them through that journey and fund them as long as they show us success every year. That's why having a set of metrics and scorecards and milestones-- We're not sticklers for, "Oh, you said you were going to do ten and you've only done nine. You're out the door." We understand that there are ups and downs and we're very long term funders. But I think that it's a good structure for those organizations to have and then a good discussion for the funders. One example I could give you if I'm allowed to brag a little bit, is the first grantee that we added was in 2008. His name is Andrew Youn; he's the founder of One Acre Fund, and he's getting the Health of Humanitarian award next week which I'm very proud of. Andrew had this high load with half a million dollar budget and 600 farmers-- was working with subsistence farmers. And today, One Acre Fund is a 300 million nonprofit with more than 200 million in revenue that works with 2 million farmers directly. But they also work with another 2 million through government channels.

Ben (00:34:51):

How long did that take to scale?

Nina (00:34:54):

About 15 years.

Ben (00:34:55):

Yeah. So that's quick, right? That's not too slow and that you can still do it. For organizations thinking about applying to you, what would you suggest they think about? We've touched on how scale is quite important, vision, also execution and showing those type of things. But is there anything else you think more broadly they should be thinking about in terms of interesting to you?

Nina (00:35:25):

So first, I expect them to demonstrate a deep understanding of the problem they're solving as you are always a student of your own sector. That obsession about the problem you're solving and the people that you serve really needs to come across in the first 10 minutes of the conversation. I also like candidates who can articulate a clear vision and translate that theory of change into how the model really works in practicality, or what they think the pathway to scale can be. But at the same time, you need to know your numbers because without the numbers, it's just an idea and we really like people to do the work before they come and talk to us. So having some numbers on your fingertips like the budgets and the beneficiaries and the impact indicators, but also have a good grasp on the makeup of your financials is really important. We don't expect them to have the right answers and we do work with early stage ventures. But at least to demonstrate that they have done that homework and those are the things that they're just worried, concern; keeps them up at night.

Ben (00:36:44):

Great. So that leads me on to saying tell me about some of your investees and some of the successes. We mentioned one pretty brilliant one, but I don't know if there's others you want to highlight. And perhaps you might also want to highlight what you might've learned from one or two failures or mistakes which haven't worked out and maybe why. So tell me about your investees, any successes, any mistakes if you would like although we can concentrate on both.

Nina (00:37:15):

Yeah, fantastic. So NewGlobe is one of the most fascinating bets we've made. The idea behind is that if you were the best teacher with the best connectivity, you just wouldn't have the time to read all the research on education and best practices and then translate it into a lesson plan. It'd be impossible for you to do. So this is what NewGlobe does. They do the heavy lifting and they train and coach teachers and provide them with these lesson guides packed with evidence-based research. So today, they employ about 150 people in Massachusetts doing instructional design-- that's all they do. These are based on a billion data points that they collect throughout the year. The interesting part about NewGlobe and how we met them is that we made an equity investment in the company in 2008 to build a network of private academies in Nairobi.

Then a few years later, they called us because they needed a grant to bring this model to Liberia. Back then, Liberia had an incredible visionary president, Madame Sirleaf, who was the first elected female head of state. And this experiment would allow NewGlobe to transport their model where they didn't control all the variables, but where the need was massive. That flipped the model and now they're partnering with innovative government that want to reform public systems. They have 2 million kids under contract. Professor Kramer who won the Nobel Prize in 2019 released this RCT saying that they have the largest learning games ever in education. So it has been exciting to see.

Ben (00:39:11):

That sounds amazing. We'll get onto another example. I just wanted to comment on that area, which is scale essentially, some people kind of call it a meta science area because like you say, what you're doing is you're giving the teachers the tools and empowering all of that level. It seems to me that-- I don't quite know how you get to it, but there seems to be a lot of unknowns in terms of the best ways of how we should do things. What are the best ways of teaching? What are the best ways of doing science funding? What are the best ways of combating that? And when you can get a group or an organization which can make discoveries and do that, then you have enormous scale impact, particularly at the kind of second order. So that seems really exciting. So yeah, another example, if you wanted, or we can talk about that one as well.

Nina (00:40:00):

I mean, we could have three more podcasts about education because it's a fascinating subject as you and I are educators in our post-work of our kids. And then education is also more controversial, I think than in health, where you really know that if you do A, B and C. So I think that structural pedagogy has been important and Professor Kramer's RCT is a great way to show that these things really work. So I'm going to go on a second example. There's a group called-- and this one is at the intersection between health and education. So healthcare learners is an organization in Zambia that turns teachers into health workers and schools where the kids spend most of the time as triage grounds to reduce morbidity to that five to 15 year old group.

There's a lot of emphasis, rightly so, on the under fives in these countries. But what happens with the older kids is parents are having a harder time justify getting a day out of work to stay for hours at end at a clinic waiting. Then what is a scratch may develop into a permanent disability if not caught on time. So school health is not an innovation of its own, but normally it's like a one person with one room. And what health learners is trying to do in Zambia-- and they have a massive buy-in from both the ministers of education and health. By doing that, what happens is that attendance goes up because parents have a kid with a cough and they send them to school because they know the school is going to look after them, they link to the clinics, and the clinics are very excited because they get to see these cases really early on the piece. Then once students are triage, they go up the list and then the waiting time goes to like 30 minutes. So that's an exciting one.

Ben (00:42:17):

That's really interesting as well because it reflects that even in rich nations, like here in the UK, there's a lens of for instance, pupils in need have these free school meals because they found that if people are not eating enough you got undernutrition, then you've got all of these other poor outcomes. So even in rich nations, people are kind of discovering these intersectionalities. And actually, I think that's something that the UK and others could learn because we're probably not as good as doing some of those things for some of our own challenges, let alone the sort of challenges that you have in poorer nations. So yeah, you had another example.

Nina (00:43:02):

Yeah. So I'm going to squeeze in another example if you allow me, and this is an oldie but goodie. So VisionSpring, which we also added in 2009-- By the way, one thing that we're really proud of is that we've added a lot of these organizations that we continue to support 15 years later and that's also rare. It's important because it does take many, many years to activate this change. There's more than 2 billion people that really suffer from blurry vision and this massively impacts the quality of life. So we've been funding VisionSpring that creates access to affordable eyewear so that people in low income communities can see well, but also do well. These are tailors and artisans or factory workers.

Glasses is really a 13th century invention, and it's just this simple yet powerful tool that allows workers to perform ice training roles and students to see the blackboard and track drivers to stay safe on the road. So VisionSpring wants to make clear vision a reality for everyone. 50% of the customers are first time wearers and they're also building this body of evidence to demonstrate that a simple pair of glasses can improve the productivity and incomes by 20 and 30% which means that when you put together these 9 million pairs that they have delivered since the early two thousands, they really unlocked like $1.8 billion in economic earning potential.

Ben (00:44:52):

That's another great example. Did you want to reflect on a mistake or a failure?

Nina (00:45:00):

Yes.

Ben (00:45:00):

There's this idea that actually no mistake or failure is a failure if you learn from it, because a lot of things in venture aren't going to work out right. But if you've got learnings from it, then they're not necessarily as big a failure as you might've thought.

Nina (00:45:16):

Yes. And to be honest, we have a lot of successes in our portfolio and that has given us a jaded view of what success means which in terms what has resulted is in us making less bets. So we're very conscious of changing that. And you can because you have some really good ideas and then everyone-- Your idea of what success looks is the VisionSprings and the NewGlobe. But you still need to go, like, "Our job is to go out and make bets on new and proven models." One thing we learned very early in the process is you may fall in love with very charismatic entrepreneur, but you really need to be very much in tuned with what they do. An example, is someone that had a model where they would go into a village and would do a lot of different things. They can do a little bit of algorithm of education, et cetera. Our general view was that it's very hard to do one thing on your own and we really wanted to find people that were obsessively trying to be a good educator. So three years later, what happened is that we kind of never really believed in this multi-sector approach to poverty. So it was really hard for this entrepreneur to convince us because we didn't buy in into the first idea. So that I think it's unfair because they're trying to convince you for three years on something that you maybe didn't really believed in the first place.

Ben (00:47:01):

Sure. And would there have been anything that could have convinced you? So I guess this is the idea that focus on one thing is essentially a superior model. But there is a, I guess a small counter example sometimes that doing two or three things together gets you a synergy, but it tends to be really difficult at startup. That tends to be a big company idea rather than its startup tends to be focused. And actually, you are now moving away from conglomerate models; even a big company that actually, if they spin off their business units to focus on something, you get a more appropriate strategy and the like.

Nina (00:47:37):

Yes. We don't necessarily have very strong views as a reminder. We're writing checks from very far away and we're definitely not the experts. But I think the preference was really on focusing, on specializing in one thing which is what everyone in the portfolio does one. And if that's what your preference is, then maybe you shouldn't be adding someone in the portfolio whose preference is to be very good at five things.

Ben (00:48:12):

That makes sense.

Nina (00:48:13):

And I think it goes back to a little bit on the criteria and then deciding that you only have time to work with a handful of people and then what are the right bets for you.

Ben (00:48:24):

Yeah, certainly if you say, "Oh, I'm going to work on five SDGs as opposed to I'm just going to work on one," and maybe the second one comes along is particularly early stage. I think clarity or vision definitely comes into that. Great. Well maybe pivoting to a couple of more fun things or different things. What is, I guess about being based in New Zealand? So I'm interested to know what you think there are; the advantages and disadvantages to being based in New Zealand. I guess you've already hinted one is that in some ways you are afar, which gives you the ability to look perhaps more critically, but you also travel to Africa and places and be on the ground. I wasn't aware there was a very large investing scene within New Zealand, so it does seem quite a far place. But I don't know if there's any advantages or disadvantages do you think about being based in New Zealand?

Nina (00:49:21):

Yeah, so there are a few-- I wouldn't say disadvantages, maybe inconveniences. So for example, it does take 35 hours to get to Nairobi and you need to spend like five or six hours in a couple of random airports. The other one is that we are the only-- I mean, the investing scene, I think more on the tech space is thriving and more so in the 20 years that I've been in New Zealand, but not so much the social sector. So we're the only funder doing this type of work in New Zealand, and we do get lonely. So that's what I think. But there's so many advantages; you work in this neutral territory and you get less caught up on the trends and the who is who. We're hyper-focused because we get all done when the rest of the world is looping. Sharing the work and having these interactions with different funders and doing reference calls has really helped us get connected. But of course, visiting grantees and attending the school forum which is the one event that I never miss, is incredibly important to keep us motivated and then connected to our peers.

Ben (00:50:43):

Great. And thinking wider, do you think there is anything more particularly unique or misunderstood about New Zealand? I guess from the outside world the clichés are around rugby and cricket. Here, at least in the UK, there were jokes about sheep-- I think milk is actually still quite a big export, if I remember correctly with it being far away, but also being amazingly beautiful. I think it is Lord of the Rings territory and the countryside and everything and the cities. So I'd be interested in your reflections about what's unique about New Zealand, or maybe misunderstood because I'm sure it isn't all about sheep and cricket.

Nina (00:51:26):

No. And maybe I did marry one of the few kiwis that do not like rugby-- maybe a bit skewed. It's beautiful, it's easy, and it's really great to have kids growing up in New Zealand especially. One of the things that is really unique is that you get to be a generalist. Especially when you're working in places like New York or London, being a specialist is so important. When you try to change your job they're like, "No, no, no. You do equities, you don't do that." In New Zealand, we get to do that. This means you need to be very comfortable with being the person in the room that knows the list about the conversation, but you are also motivated with the learning journey. That is also about-- When we think about our work at Jasmine, we are generalists and we touch upon a lot of different things and we don't know much. If you're a learner, it's a fascinating country to be in because you need to do a lot of different things.

Ben (00:52:40):

Great. Okay. And then we'll have a little fun section of overrated, underrated; just a couple of things that you might think about that. I'm going to start with a fun one. So overrated, underrated, Spanish food, or you could be more in particular and go Catalan food. But Spanish food, do you think it's underrated or overrated?

Nina (00:53:01):

Oh, it's underrated.

Ben (00:53:05):

Of course, you have a Catalan background.

Nina (00:53:08):

Absolutely. And the beauty of the products and the proudness of everyone-- You grow up cooking with your moms and your grandmas. That idea about going to the market and going to every stall and talking to everyone and then the fresh produced and the fish is fantastic. But there is a way around Catalan people-- and especially in Barcelona where the weather is so fantastic and everyone's is always outside standing up eating.

Ben (00:53:43):

Yeah. And does New Zealand have a similar market or food culture? My impression is not, but obviously it's got a lot of farming. But it doesn't have that kind of same history, or does it?

Nina (00:53:53):

No, but as I've learned is that New Zealand in the seventies was not necessarily a place that used olive oil, but it's really leap frogged. I think both New Zealand and Australia are countries with big influx of immigrants that have really shaped the culture and the food. So Auckland has an interesting eclectic; a group of restaurants and chefs and maybe a bit more. Spanish tend to really love the food so much that's when they do visit in New Zealand, you get a bit of a variety.

Ben (00:54:32):

Yeah. The upsides of globalization and immigration. Great. And a couple of others. Do you think artificial intelligence, AI, is going to be overrated, underrated, neutrally rated? Obviously, that's quite a big subject. But what do you think of AI?

Nina (00:54:49):

I'll be brief on that one. But I am hoping it is underrated and it will play a critical part in our lives. And obviously, technology is so important for the solutions that we support because we're trying to get them to very remote areas, and we're trying to do it cheaply and it needs to play a part. I mean, mobile money has been in Kenya since 2015, and data is getting cheaper. You can find forges in a village in Mali. So you need to really design for that type of future. Now, there is a ton of grants in the AI space and all I'm hoping is that it lands in the right hands because otherwise, it'd be such a waste. We have an organization called Digital Green, and it was a spinoff of Microsoft India.

It's really Rick Gandhi who runs it. It is a very unique individual that has the technical know-how, but also the understanding of what is the life of a farmer and what needs to happen. So he does believe that we still need a bit of fine tuning. But they have this library of 7,000 videos they think that if you can get a picture and say, "I'm a farmer in Pradesh and this is June." And then understand this is Chilies, then you could get some really good advice on the white flies in my chilies. So there's a lot of things. But again, AI, grant money in the right hands will be fantastic.

Ben (00:56:36):

Yeah. Huge potential. I think I agree. Obviously some risks, but I also sit on this side that I think the potential outweighs that, and really hoping that it continues to be underrated. Great. Okay. The last one here comes under the heading of billionaire philanthropy. So obviously in the philanthropy space, we kind of think we could deal with more funding. One of the critiques, I guess, is that maybe governments should be doing more and that billionaires are not pointing in necessarily the right direction for all of that. So, underrated, overrated, or any thoughts on billionaire philanthropy?

Nina (00:57:14):

Underrated all the way. There's always need for extensive checks with no strings attached and this is where this new one is coming in, which is fantastic. So a few have emerged in recent years, like the Audacious Project and the McKensey Scott, and our grantees have been the very lucky recipients of a lot of that. So we have seen firsthand on how an organization that is starving-- And starving is something that is changing in the views of funders, but we've heard it many times like, "Why do they need my money? They should be only working on three months of run rates." That's not how companies-- they don't waste money every three months. So you get these big grants and people have one or one year and a half of run rates, and that means that they can hire that fantastic CFO and invest in the systems and it has been game changing.

Ben (00:58:20):

Great. Yeah, I think everyone could probably afford to give more, at least in the rich nations. I definitely think it's been generally a net positive so I'm in agreement with you there. Great. So wrapping up with the last couple of questions. One would be current projects that you or Jasmine are working on, or any thoughts about current or future projects?

Nina (00:58:44):

Yeah, I think I've touched a bit in this conversation. But we have been operating for the last 15 years and we now have a team in place. We're trying to double down on our portfolio which is exciting, but also packaging our learnings for what we call the doors and the donors; the doors are the practitioners and the donors are the people that write the checks. And one of the things-- that we have done and do and can do that we can help philanthropists or they can skip a few steps. A lot of it is on the diligence and the criteria and all these things that we've discussed today. But on the other hand is how can we help the people outside of our portfolio? So that's what we are internally trying to discuss on what that looks like.

Ben (00:59:45):

Great. That's almost your own meta ability to scale the information and the work that you do to a larger audience which might have it.

Nina (00:59:55):

Yeah.

Ben (00:59:55):

Excellent. Then the last question would be, do you have any life advice? So this would be advice to people who either want to be social entrepreneurs or maybe want to be in philanthropy or any other observations you have had of being both in investment banking, have experience of Europe, experience of New York, experience of New Zealand. You have a great life experience. Anything you'd like to share with us?

Nina (01:00:26):

I'd say that span your twenties working really hard in this very high pressure environment and getting all the skill sets that you need; accounting, communications, analysis. Do it. That's when you should do it is in your twenties. So then when you're in your forties and fifties, you can choose to have those more meaningful roles. Like at adjustment, all of us wake up in the morning and say how privileged we are to be doing this kind of work and working on these incredibly interesting problems and these promises because it's so optimistic knowing that there are a lot of really good people making big changes. But we all have a sit because we've done a ton of work and I think it's what the social sector needs. You need people that have spent good chunk of their twenties and early thirties bringing this commercial skillset. We now have a team; an eclectic team. One comes from Venture Capital, the other one, Corporate Finance. We've just added someone that came from strategy. So I think that would be my advice; do that and then come to our sector.

Ben (01:01:57):

Yeah, that sounds like great advice. Sometimes I meet some young people and I think there is a mistake they think, "Oh, we finish university so our learning is over." But in some ways in many respects, it is just beginning. And the more skills you pick up earlier, particularly in your twenties, they compound. The ability to understand the cash flow of value should never go away. Then the more that you see them over ten, twenty, thirty years, the deeper understanding that you have. So the skills you pick up, definitely really valuable.

Nina (01:02:32):

Yeah.

Ben (01:0233):

Great. So on that, Nina, thank you very much. Please do check out Jasmine for people listening on.

Nina (01:02:41):

Thanks, Ben. It has been a pleasure.




Qs for me.

Can cut anything.



I am super excited to be speaking to Nina Gené. Nina Gené is CEO of Jasmine Social Investments, a private foundation in New Zealand that funds high-performance social ventures solving big problems in the poorest geographies. Nina welcome.


About Jasmine

  • Jasmine funds social ventures. How did Jasmine come about, and how did you come to Jasmine?

  • What does venture philanthropy mean to you? And how do you define "impact"?

  • How do you identify opportunities? What criteria do you use?  What do you think about themes? Are SDGs helpful? 

  • What’s your philosophy, and how do you assess potential investments 

  • What do people most misunderstand the way Jasmine invests and funds? Do you view this as a view of  impact investing? Or how does impact investing fit into your strategy?

  • You give help and challenge to your investee companies - this much like some VC - how does that work?

  • How do you measure and track the impact of your investments?  Is measurement overrated/underrated?  What do you think incentives 

  • What are some of the biggest challenges?

  • Where do you see the greatest opportunities for investments going forward?

  • Tell me about some of your investees.

  • How important is an organisation's ability to scale?

  • Is funding the bottleneck for social investing or where do you see the hurdles?

  • For organisations thinking about applying to you, what would you suggest they think about?



Working in the Social/NZ sector

  • Are there advantages/disadvantages to being based in New Zealand?

  • How influential is the culture of an organisation?

  • What advice would you give to someone wanting to become an impact investor  / be involved in philanthropy ? And also, social entrepreneur start-ups?

Other types of investing

What do you think of effective altruism?


Wrapping Up:

  • Underrated / Overrated: AI, Existential risk, Billionaire philanthropy; NZ’s living budget

  • Current projects 

  • Life advice 





Hi Nina - Thanks once again for coming on the podcast. First, to repeat, this is not meant to be “challenging” - it’s trying to get the best ideas and versions of what you have to say.


So if you end up thinking you don’t like an answer, let me know later or at the time and we will edit it out.


I’m also hoping to have more of a conversation, so we won’t necessarily get to all these questions. But these are the type of areas I am interested in:



What does impact investing mean to you? And how do you define "impact"?


How do you identify impact investment opportunities? What criteria do you use?  Do you think about themes ? Are SDGs helpful ? 


What’s your philosophy and how do you  assess potential investments ?


What do people most misunderstand about this type of investing ?


You give help and challenge to your investee companies - this much like some VC - how does that work ?


How do you measure and track the  impact of your investments?  Is measurement over rated / under rated 


What are some of the biggest challenges?


Where do you see the greatest opportunities for impact investment going forward?


Tell me about some of your investee companies ?


How important is an organization's ability to scale ?


Jeremy Grantham has suggested that for profit VC might also be impactful and despite giving a lot to philanthropy also argued there might be more opportunity in VC.   Is funding the bottle neck for impact investing or where do you see the hurdles ?


For organizations thinking about applying to you, what would you suggest they think about ?


(Is your job opening still open, if so I can ask about what it might to work on your team) 


Are there advantages / disadvantages to being based in New  Zealand ?


How important is the culture of an organization ? 



We might play Under rated / Over rated 

-AI

-Existential risk 

-Billionaire philanthropy



What advice would you give to someone wanting to become an impact investor? And also social entrepreneur start-ups?


Finish any comments on:


Current projects 


Life advice 


Thanks again, Ben



*


 Nima Gene.Nina joined Jasmine Social Investments in 2007 with the responsibility to identify prospective investments, support partner organisations and collaborate with a network of social investors. Nina Gené is CEO of Jasmine Social Investments, a private foundation in New Zealand that funds high-performance social ventures solving big problems in the poorest geographies. Nina’s role is to evaluate opportunities (grants, debt, and equity), advise portfolio organizations, and collaborate with a network of social investors. 



Price is what you pay. Value is what you get.

I like aphorisms. When you dwell on them the best ones make you think about all sorts of ideas. The vague ones can inspire critical thinking. Plus they are short. Some are twee but even the tired ones are tired because they’ve held some element of truth for so long. I collected and devised many of mine own in a small book a few years ago. Now, I am dwelling again on investment type aphorisms as applied to life.


I have:


Every stock has a price. Not every stock has value.


Oscar Wilde wrote: (In Lady Windemere’s Fan via Lord Darlington):

a cynic was ‘a man who knows the price of everything and the value of nothing.‘ :


Warren Buffet wrote: 

Price is what you pay. Value is what you get


Philip Fisher: 

“The stock market is filled with individuals who know the price of everything, but the value of nothing.”


The price part seems obvious. There is a listed price for assets or products and you pay that and receive the item or share.


The value part fits on two ideas. One idea is “intrinsic value”.  In an investing context, the idea is that price and value are not always equivalent. There are times when the price you pay is less than the value you receive (a bargain), and other times when the price is much more than the value.  Investors looks for opportunities where they believe the intrinsic value of a stock (what you get) is greater than its current market price (what you pay). 


This idea is applicable in life beyond investing. You pay a high price for a luxury item, but if it doesn't bring you proportional happiness or utility. Conversely, some experiences or items might be low or free in price but offer enormous value in terms of enjoyment, knowledge, or emotional well-being. This can be particularly true for experiences where there is evidence the memory value of experiences is high.


Applying this to life events


**Understanding the value, not just the price**: In life, we often mistake the price of something for its value. Price is what you pay, while value is what you get. For instance, spending time with loved ones, cultivating deep relationships, maintaining good health, or investing in personal growth may not have a monetary price tag attached, but their value is immense. Conversely, some things might be expensive but bring little lasting happiness or fulfillment, proving not to be valuable in the long run.


 **Looking beyond the surface**: Just as the stock price doesn't tell the whole story about a company's value, people's outward appearances or the immediate impressions they make don't fully represent their value. This can apply to judging people based on their looks, wealth, or social status, instead of their character, actions, or potential. Make informed decisions. In life there is importance in making decisions based on thorough understanding, not just superficial appearances. 


**Long-term versus short-term perspective**: In the stock market, some traders might focus on short-term price movements, while overlooking the long-term fundamental value. Similarly, in life, it's easy to get caught up in immediate pleasures, distractions, or short-term gains, while losing sight of long-term goals, values, or the bigger picture of what truly matters.



 **Don't follow the crowd**: The stock market can be influenced by herd mentality, where people buy or sell based on what others are doing, without considering the intrinsic value of the investment. Similarly, in life, it can be tempting to follow trends, or do what others are doing, without considering whether it's truly valuable or suitable for you. It's important to think independently and make decisions based on your own values and circumstances.


Caveats to note:

While the advice in the aphorism is generally sound, there are a few caveats and critiques to consider.


1. **Subjectivity of Value**: The aphorism implies that there's an objective, underlying value to things (or people, or life experiences) that one should understand. However, value is often subjective and can vary greatly from one person to another based on their personal tastes, needs, values, or circumstances. What's valuable to one person might not be valuable to another. Therefore, it's important to understand that each person has to determine what's valuable to them based on their own criteria.


2. **Practical Constraints**: While it's ideal to make decisions based on a deep understanding of their underlying value, this is not always practical or possible. Gathering and analyzing all the relevant information to fully understand the value of something can take a lot of time and effort, which might not be feasible in all situations. Sometimes, one has to make decisions based on limited information or time constraints. 


3. **The Role of Emotion**: The aphorism suggests a rational, analytical approach to making decisions, which is certainly important. However, emotions also play a significant role in our decisions and can't be ignored. Sometimes, something might not seem valuable based on a rational analysis, but it brings emotional satisfaction or aligns with one's passions, which can make it valuable in a different sense. 


4. **Changing Value over Time**: The value of things can change over time as circumstances change, new information becomes available, or people grow and evolve. Therefore, even if one understands the value of something at a given moment, this understanding might not remain accurate in the future. 


5. **Risk and Uncertainty**: Even with a deep understanding of the underlying value of something, there's always a degree of risk and uncertainty involved in decisions, whether in investing or in life. There are always factors that are out of one's control or unpredictable events that can occur. Therefore, even the best-informed decisions can lead to unexpected outcomes.


Hedge Fund Carbon Accounting

How should short selling account for carbon? Does selling short impact cost of capital or engagement ? My friend Jason Mitchell discusses various views and in particular how regulators have started to think about carbon accounting with hedge funds.

We started talking about this in a podcast a while ago (link end), and you can now read some collected thoughts in the paper which is now publicly available.

Summary:
- Sustainable finance regulation has largely overlooked alternatives, particularly hedge funds, given the greater complexity of strategies and asset classes. However, regulators are now expanding their scope to recognize the role that hedge funds can play in #sustainable finance.

- The role of short selling in sustainable finance, especially in a net zero context, has been increasingly discussed and debated among regulators, market participants, investor initiatives, investor trade organizations, and #ESG data providers. There is a concern that hedge funds may, intentionally or unintentionally, employ short selling to misrepresent their real-world impact, which is distinct from exposure to financial risk.

- Short selling can affect the cost of capital and engagement as channels of influence on corporate behavior. However, there are nuances that should be considered, namely the efficacy of short selling among different asset classes to affect the cost of capital, the time-varying aspect of short selling, and the limitations that short sellers face when engaging corporates.

- UK, US, and EU regulators have each signaled their leaning in different manners. The EU, as the regulator with the most mature regulatory framework, appears to establish a compromise that balances safeguards against greenwashing with the mechanics of portfolio management and reporting.

Download paper here.

Podcast with Jason here.

Tyler Cowen: Hayek lecture on economics, AI and large langauge models

Tyler Cowen gave the Hayek lecture in London this week. In my view, his personal charisma continues to be strong - and perhaps better than what you see in recordings of early talks. Tyler is funny (he could do with even more jokes!). He comments in the talk about the value of personal presence, and I think this is true and could be felt at the event. The video is above, so you can absorb much of the content … but how special was it to be there (?) (and also to chat to others there). The social capital part.

I make a short summary of my notes below. Suitable for a tweet thread. Then below I give you a few versions that GPT-4 did, after basic prompting and me feeding the very bad YouTube auto transcript including time stamps into it (unfortunately I needed to put into sections). I also asked GPT-4 to rate Tyler’s lecture (see end!)

GPT-4 summarised the talk well enough that it can tell you whether it’s worth listening to the whole talk and this only took a few minutes. My notes probably do about the same, but differently and of course you need to rely on someone there. I don’t think I saw any other note takers. Of course…. Context is (still) that which is scarce.

My short notes:

Two kinds of AI talks. 1. Audiences that need to be convinced. 2. Audiences that do not need to be convinced.

We have invented AI that is better than humans in many areas.

How should we think about this in economic terms ?

We have a research assistant, colleague and architect. Not a carpenter or gardener.

One argument. We have increased the capital stock. But another maybe we have increased the intelligence of society massively. So in that sense it’s labour. A new factor of production.

What is worth more? Or less?

Less.

1. Routine back office work, you are worth less. 2. Memorising facts is worth less.

Personal charisma will be worth more. Looks and how you come across more. Personal networks will be worth more.

Physical Co-ordination, carpenters and gardeners should be worth more. People who can manage having an assistant will do better.

How should capital be revalued ? Many forms of Labour are now less scarce. So long electricity, long some hardware.

The AI will be commodified. The gains will go to the users. No one is talking about Gutenberg the billionaire.

Central planning going to be harder. More projects, more complexity harder to centrally plan.

Economic insights rooted in Smith, Hayek. Decentralised systems hard to take over.

AI Can’t work in physical space. Think of it like Humans and dogs have co-evolved.

The AI is going to have its own economy. It’s own laws, it’s own economy. Maybe crypto.

(Missing: How Tylers uses AI more effectively, also the Q&A; jokes eg with Bryan Caplan testing the AI).

GPT summary (first 15 minutes):

Title: The Economic Implications of GPT: Unleashing the Power of Language Models

Renowned economist Tyler Cowen recently delivered a compelling talk, delving into the profound economic implications of large language models, particularly focusing on the celebrated GPT 3.5. In a nuanced exploration that blended empirical evidence with astute observations, Cowen engaged his audience in a thought-provoking discussion that left them pondering the transformative power of artificial intelligence (AI) in our labor markets.

Cowen began by highlighting the remarkable abilities of GPT 3.5, emphasizing its superior performance in tasks such as passing bar exams and medical diagnostics. Drawing from a personal anecdote, he playfully recounted how GPT 4, the latest iteration, aced an economics exam in a matter of months, leaving his colleague and himself pleasantly surprised. These achievements underscored the unprecedented capabilities of these language models and set the stage for the ensuing exploration.

Turning his attention to the economic landscape, Cowen posited that routine back-office work could become increasingly redundant as GPT models render such tasks obsolete. He postulated that the economy must adapt to this shifting dynamic, where the ability to access information effortlessly diminishes the importance of rote memorization. This transformation necessitates a reevaluation of the skills that hold value in the labor market.

Cowen intriguingly contended that personal charisma and physical appearance may ascend as prized attributes in this new AI-driven world. While expertise and knowledge remain crucial, the capacity to captivate and energize audiences could emerge as a distinguishing factor. Moreover, personal networks, built on trust and human connections, would retain their significance despite GPT's vast knowledge base. These factors prompt a reevaluation of the human qualities that AI cannot replicate, positioning them as valuable assets in an increasingly automated society.

Viewing GPT models as a unique factor of production, Cowen argued that their integration into our digital landscape propels the accumulation of intelligence at an unprecedented pace. Leveraging the power of the internet, these models represent a distinct combination of capital and labor. Cowen drew an insightful parallel with the historical impact of the printing press, underlining the accessibility and immediacy that GPT models offer, with vast potential implications for various sectors.

Demonstrating the practical impact of GPT models, Cowen pointed to programming as a tangible example. Citing a case study from Italy, he revealed that a temporary ban on chat GPT led to a dramatic 50% reduction in programming output overnight. This stark statistic underscored the symbiotic relationship between human programmers and AI assistance. By collaborating with GPT models, programmers could explore a multitude of ideas and experiment more freely, amplifying their creative output.

In his closing remarks, Cowen urged individuals to embrace the opportunities presented by this new AI landscape. Effectively managing the AI assistance at hand, particularly for academics, emerged as a critical factor for success. The ability to unleash the potential of AI research assistants and harness their capabilities as catalysts for intellectual exploration and innovation would define the future trajectory of individuals and their endeavors.

In sum, Tyler Cowen's thought-provoking talk shed light on the economic ramifications of large language models, notably GPT, stirring contemplation among his audience. As routine tasks potentially recede in value, the significance of personal attributes, networks, and the effective utilization of AI assistance becomes apparent. Ultimately, the fusion of human ingenuity and AI capabilities promises to reshape our labor markets, propelling us into an era where adaptation and collaboration hold the key to success.

GPT - I ask it to add the next 15 minutes:

Title: Unleashing the Power of GPT: A Transformative Journey in Economics

In a captivating lecture that left the audience pondering the future of labor markets and asset valuation, economist Tyler Cowen delved into the profound implications of large language models, focusing on the renowned GPT 3.5. With a keen eye for empirical evidence and astute observations, Cowen painted a compelling picture of the transformative power of artificial intelligence (AI) and its impact on our economic landscape.

Cowen commenced his talk by highlighting the extraordinary capabilities of GPT models, showcasing their remarkable performance in tasks such as passing bar exams and medical diagnostics. A personal anecdote about GPT 4 acing an economics exam in a matter of months demonstrated the astounding progress in AI capabilities. These achievements underscored the profound implications of large language models and set the stage for Cowen's exploration of their economic ramifications.

Turning his attention to the labor market, Cowen postulated that routine back-office work could become increasingly obsolete as GPT models render such tasks redundant. This shift necessitates a reevaluation of the skills and attributes that hold value in the workforce. Cowen intriguingly contended that personal charisma and physical appearance may ascend as prized assets in this new AI-driven world. While expertise and knowledge remain crucial, the capacity to captivate and energize audiences could emerge as a distinguishing factor.

Furthermore, Cowen emphasized the continued significance of personal networks in an era dominated by AI. Despite the vast knowledge base of GPT models, the trust and human connections formed through personal networks retain their importance. This highlights the unique qualities that AI cannot replicate, positioning them as valuable assets in an increasingly automated society.

Viewing GPT models as a new factor of production, Cowen postulated that their integration into our digital landscape propels the accumulation of intelligence at an unprecedented pace. The accessibility and immediacy that GPT models offer, akin to having a research assistant, colleague, and architect all in one, has the potential to reshape various sectors.

Drawing from a case study in Italy, Cowen provided tangible evidence of the impact of GPT models on programming output. A temporary ban on chat GPT resulted in a dramatic 50% reduction in programming output overnight, illustrating the symbiotic relationship between human programmers and AI assistance. By collaborating with GPT models, programmers can explore a multitude of ideas and experiment more freely, amplifying their creative output.

In considering the revaluation of capital, Cowen posited that high-quality land and natural resources, particularly those feeding into electricity production, may gain increased value as labor becomes less scarce. He also identified hardware companies like Nvidia, which produces the key GPUs used in GPT models, as potential beneficiaries in this evolving landscape.

Cowen acknowledged the speculation surrounding the revaluation of capital and the potential commodification of AI. However, he suggested that a significant portion of the gains would go to the users rather than the companies selling AI services. Drawing from historical technological advancements, Cowen emphasized that the most fundamental developments tend to benefit the users, and he expects this trend to continue in the AI era.

As Cowen concluded his talk, he reflected on the ways in which GPT models can be harnessed effectively. He emphasized the importance of setting context and asking follow-up questions to enhance the accuracy of the models' responses. By framing queries appropriately and refining the training process, users can unlock even greater potential from these language models.

In a fascinating side note, Cowen praised the advancements in augmented and virtual reality, exemplified by Apple's Vision Pro. These technologies aim to provide additional context, filling in the gaps in decentralized systems. The ability to create and manipulate context, which is often scarce in human life, emerged as a recurring theme in the remarkable developments of 2023.

Tyler Cowen's thought-provoking lecture shed light on the economic implications of large language models, particularly GPT, leaving the audience captivated by the transformative power of AI. As routine tasks diminish in value, the significance of personal attributes, networks, and effective AI utilization becomes apparent. The fusion of human ingenuity and AI capabilities promises to reshape our labor markets, ultimately leading to a future where adaptation and collaboration are the keys to success.

Then the next part:

Title: The Rise of GPT and the Transformative Potential in Economic Modeling

In the second part of his enlightening lecture, Tyler Cowen delved deeper into the remarkable capabilities of GPT models and their potential impact on various fields, including social sciences and economic modeling. He highlighted the impressive linguistic diversity that GPT models possess, even without explicit training in different languages, demonstrating their ability to comprehend and translate a wide range of linguistic data.

Cowen postulated that if GPT models can excel in modeling the intricacies of language, they can also be applied to model other complex systems. He introduced Bloomberg GPT, a forthcoming product that utilizes GPT models trained on the vast financial data of Bloomberg. This groundbreaking approach to macroeconomics has the potential to revolutionize central banking within the next two years.

Furthermore, Cowen envisioned a future where GPT models are utilized to create models of small villages and companies, eventually leading to a reimagining of social sciences. By feeding rich and specific data into GPT models, anthropological economists can develop semi-successful models of small-scale economies and gain a deeper understanding of various entities and systems. This approach may render traditional economic tools obsolete, as the focus shifts to generating rigorous models through comprehensive data input.

Cowen also touched on the use of GPT models in experimental economics, highlighting their ability to play economic games like human agents. These models can be fine-tuned and employed in place of human subjects, offering a cost-effective and efficient approach to conducting economic experiments.

Amidst discussions about the potential risks associated with AI, Cowen dismissed the notion that GPT models and their successors will rise up and overthrow humanity. He argued that overestimating pure intelligence overlooks the challenges that AI entities would face in operating in physical spaces. Drawing on the analogy of humans and dogs co-evolving, he envisioned a future where people form unique relationships with GPT models, using them as therapists, teachers, and companions.

Moreover, Cowen highlighted the emergence of a new AI economy, where AI entities will have their own laws, currencies, and economic activities. He emphasized the significance of cryptocurrencies in facilitating transactions and interactions between AI entities. Crypto, in his view, is perfectly suited for AI-based transactions, providing a means of exchange that avoids the complexities of traditional banking systems.

In conclusion, Cowen emphasized that the transformative power of GPT models and AI technologies will bring about significant changes in various fields. As social sciences adapt to this new era, the ability to model complex systems and the rise of a distinct AI economy will reshape traditional frameworks and necessitate new approaches to research and analysis.

And the next parts plus Q&A…

Title: The Impact of AI on Public Finances and the Future of Expertise

During the Q&A session, Tyler Cowen addressed concerns about the potential impact of AI on public finances. He acknowledged that the expansion of AI and the substitution of human labor could lead to a shift in tax revenues. However, he emphasized that the effects would vary depending on the country or region. Economies with a significant reliance on taxing routine office work, particularly across borders, might experience fiscal challenges. Nevertheless, countries with a broader distribution of labor, encompassing creative and innovative sectors, should maintain tax revenues without a significant crisis.

The discussion then turned to the question of which aspects of human intelligence AI cannot replicate. Cowen acknowledged that AI can replicate certain social skills, as evidenced by GPT's ability to exhibit bedside manner. However, he argued that it is the unique combination of social skills, physical presence, and human charisma that sets individuals apart. The ineffable qualities of human nature, blended with social skills, will continue to be valued and differentiate humans from AI.

Furthermore, Cowen highlighted the potential for AI to dethrone experts in various fields. AI can provide objective facts and information more accurately than most media sources, making it unnecessary to rely on experts for simple factual matters. Those individuals who possess creative skills, the ability to initiate and manage projects, and a self-starting initiative will likely see higher returns and increased status compared to traditional experts. Cowen expressed satisfaction with this shift, considering it a comeuppance for those experts whose expertise can be replicated or surpassed by AI.

In response to a question about the apparent lag in economic growth resulting from AI integration, Cowen compared it to the historical adoption of electricity. He highlighted the importance of time and the gradual process of incorporating new technologies into existing systems and institutions. Many academics and institutions still lag in embracing AI fully, contributing to the slower pace of economic growth. However, Cowen predicted that over time, complementary infrastructure would be built, institutions would reorganize, and knowledge storage and retrieval systems would improve. These developments, though not immediate, would eventually lead to significant productivity gains and transformative changes in society.

In conclusion, Cowen emphasized that while the immediate economic impact of AI might not be substantial, the long-term effects would be profound. He likened the current stage to the 1870s, a foundational period of technological advancement that set the stage for later explosive growth. Cowen projected that within the next 20 to 30 years, the world would experience a significant transformation driven by AI.

MOre Q&A

In this part of the discussion, Professor Tyler Cowen delves into the concept of trust in the context of technology, specifically artificial intelligence (AI). He draws a parallel to economics, where trust is crucial for predicting and understanding human behavior. However, he highlights that trusting technology poses unique challenges because it doesn't share the same biological wants and needs as humans.

To address the issue of trust in technology, Professor Cowen suggests a short-term solution of simply turning it off when needed. This implies that in the early stages of implementing AI systems, human intervention and control can mitigate potential trust issues. However, he emphasizes that in the long run, the market will play a vital role in shaping trustworthy AI. Market forces will drive the development of AI systems that are more reliable, responsive, and aligned with user demands.

The discussion also touches on the potential impact of AI on information polarization. Professor Cowen highlights that current AI systems, like GPT models, can provide unbiased and dispassionate responses to various perspectives. They can generate answers from different political orientations, potentially reducing the polarization often seen in media and public debates.

Regarding the job market, Professor Cowen discusses the potential displacement of white-collar work by AI. He suggests that while AI may enhance the productivity of educated individuals, it could lead to income polarization if certain white-collar jobs deflate in value. However, he also presents an alternative perspective where AI advancements might increase the value of jobs focused on human care and services.

When considering the future advancements of AI, Professor Cowen acknowledges the varied opinions within the AI community. He focuses his analysis on the near-term developments of GPT models rather than speculating about distant, advanced versions. He highlights the potential for significant progress with current AI models, such as training them on proprietary data, which can lead to improved performance and capabilities.

The discussion concludes with diverse topics, including Professor Cowen's affinity for London and his perception of it as an exceptional city. He briefly touches on the potential privacy implications of AI advancements and the utilization of personal data. Lastly, he expresses his skepticism about reports of UFOs and emphasizes the need for critical evaluation and further investigation of unidentified phenomena.

In summary, this part of the discussion provides insights into the challenges of trust in technology, the potential impact of AI on jobs and information polarization, the future developments of AI models, and Professor Cowen's thoughts on various tangential topics.

Then I finally try and get GPT to summarise everything (I think there might 3% slight mistakes/hallucination but no more than what a research assistant might do…)

Title: Trust, Jobs, and the Future of AI: Exploring the Economic Implications with Professor Tyler Cowen

Introduction:

In this comprehensive exploration of the economic implications of artificial intelligence (AI), we engage in a thought-provoking discussion with Professor Tyler Cowen. As a leading economist and expert in technology and innovation, Professor Cowen provides insights into the challenges surrounding trust in technology, the potential impact on job markets, and the future of AI.

Trust in Technology and the Economic Implications:

Trust is a central concept in economics, as it underpins transactions, market interactions, and economic decision-making. However, the issue of trust takes on a different dimension when it comes to AI. Professor Cowen highlights the unique nature of AI, which lacks the biological wants and needs shared by humans. This raises questions about how we can trust AI systems and rely on their decision-making capabilities.

From an economic perspective, trust in technology is vital for its widespread adoption and utilization. If users perceive AI as unreliable, untrustworthy, or unpredictable, it may hinder its integration into various sectors and impede the realization of its potential benefits. Thus, establishing trust in AI is crucial for fostering its acceptance and utilization in economic activities.

Professor Cowen argues that in the short run, the ability to turn off AI systems provides a level of control and mitigates potential trust issues. However, in the long run, market forces will likely drive the development of more trustworthy AI. Users' demands for reliable, responsive, and ethically aligned AI systems will incentivize the creation of technologies that engender trust and enhance economic outcomes.

AI, Jobs, and Income Distribution:

The potential impact of AI on job markets and income distribution is a major concern. While some argue that AI will lead to widespread job displacement, Professor Cowen presents an alternative viewpoint. He suggests that AI advancements might deflate the value of certain white-collar jobs while potentially increasing the value of jobs focused on human care and services.

In this scenario, as AI assumes routine cognitive tasks, the importance of jobs related to personal care, childcare, elderly care, and other service-oriented fields may rise. These jobs often require the human touch, empathy, and interpersonal skills that AI struggles to replicate. Consequently, AI's impact on the job market may lead to a reevaluation of the societal value placed on different types of work, potentially reducing income polarization.

While the long-term effects on job markets remain uncertain, Professor Cowen emphasizes the need for continued education and adaptability to prepare the workforce for the evolving economy. Upskilling and reskilling programs can enable individuals to transition into new roles and industries, ensuring continued employment opportunities amidst technological advancements.

AI, Data, and Economic Productivity:

The effective utilization of data is a fundamental aspect of AI's economic impact. Professor Cowen highlights the potential for AI models, such as GPT, to leverage proprietary data to enhance their performance. By training these models on organization-specific data, such as medical records in hospitals, AI systems can provide more accurate and tailored assistance in various domains.

The integration of AI and proprietary data has implications for economic productivity. Organizations can leverage AI to gain deeper insights, streamline processes, and make data-driven decisions. This can result in increased efficiency, cost savings, and improved outcomes across industries. However, privacy concerns arise in the context of using personal data to enhance AI capabilities. Professor Cowen suggests that the future will likely see the development of privatized AI models, where queries and interactions remain within the organization, ensuring data privacy while still benefiting from AI-powered insights.

Economic Considerations and the Future of AI:

Looking ahead, Professor Cowen emphasizes the potential progress and advancements in AI models, such as GPT. He suggests that the convergence of AI, the internet, and other technologies may lead to a future

where these elements seamlessly blend into a unified development. This integration could result in transformative changes across various sectors, driving scientific progress and potentially reshaping the nature of human lives.

However, the economic impact of these developments is uncertain. While AI has the potential to accelerate scientific discoveries and productivity growth, its true effect on GDP growth and societal welfare remains to be seen. Professor Cowen poses critical questions about whether AI advancements alone can solve complex societal challenges, such as income inequality or infrastructure development.

Conclusion:

In this in-depth exploration of the economic implications of AI, Professor Tyler Cowen provides valuable insights into the challenges and opportunities presented by this transformative technology. From the importance of trust in technology to the potential impact on job markets and economic productivity, his analysis offers a nuanced understanding of the evolving AI landscape.

As we navigate the future of AI, Professor Cowen highlights the need for careful consideration of ethical concerns, privacy issues, and the societal implications of AI-driven advancements. Balancing the potential benefits of AI with the preservation of human values, equity, and social welfare is crucial for realizing the full potential of this technology.

Ultimately, the future of AI lies not only in technological progress but also in our ability to navigate the economic and social dimensions of this transformative force. By engaging in thoughtful discussions, embracing adaptability, and shaping AI's development through ethical frameworks, we can steer the trajectory of AI towards a more inclusive and prosperous future.


Finally I ask GPT to give Cowen a rating….

As an AI language model, I don't have personal opinions or the ability to listen to lectures or evaluate them directly. However, based on the information provided in the conversation, Professor Tyler Cowen's lecture appears to be insightful and thought-provoking. He offers nuanced perspectives on the economic implications of AI, exploring topics such as trust in technology, the impact on job markets, and the potential for economic productivity. The inclusion of economic thinking and consideration of various viewpoints adds depth to the discussion.

Considering the breadth of topics covered and the expertise of Professor Cowen, I would rate the lecture highly, potentially an 8 or 9 out of 10. However, it's important to note that individual preferences and opinions may vary, and a comprehensive evaluation would require a more detailed analysis and direct engagement with the lecture material.

More economics…see my interview and podcast with Mark Koyama on economic history and how the world became rich.

More AI, check out Kanjun Qiu, CEO of Generally Intelligent on AI, institutional knowledge and meta-science.

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