Gregory Ho of Spring Mountain Capital joins Nick to discuss The Former CFO of McKinsey on AI, Quantum Computing, BioEngineering, and Why Social Impact Can be the Key to Investment Success. In this episode we cover:
- AI’s Applicability Across Sectors
- Challenges and Opportunities in AI Adoption
- Consulting Services and AI’s Impact
- Practical Applications of AI in Healthcare
- Investing in Bioengineering and Social Impact
- Challenges and Opportunities in Social Impact Investing
- Future of Quantum Computing and AI
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0:18
Gregory Ho joins us today from New York City. He is President, COO, and Head of Social Impact at Spring Mountain Capital, a growth-stage venture firm investing in technology-enabled businesses and healthcare companies across North America. Prior to Spring Mountain Capital, he was a Principal and the Chief Financial Officer at McKinsey & Company, advising on corporate finance and strategic planning. Gregory, welcome to the show!
0:45
Thank you. Thank you, Nick, and thank you for having me. Well, it’s
0:48
such a pleasure to meet you, sir. I’d love to hear a bit about your story and your path to becoming a VC. It’s
0:54
a long story. Starts in Hawaii, ended up coming out east. My parents kept on sending me out here, and wanted me to go to law school, so I started as a tax associate in a law firm in New York City. Kept on trying to get back to Hawaii, but one thing or another kept me in interesting things. I next went to McKinsey and Company and did several things there, which broadened my perspective substantially. I helped the company open up offices in 30 odd countries over 16 years. And among other things, I was became very involved in co founded the McKinsey Investment Office with some other partners at the firm. That led to my eventually deciding to take an open another chapter in my life, and I started Spring Mountain Capital with Lonnie Steffens in 2001 with the focus on a type of investing that we experimented with at McKinsey, which was special situations investing.
1:51
Got it. And was that the focus of the investment group then at McKinsey was a special situations private equity. Was it on the venture side? You know what? What sorts of assets, were you? Were you backing there? Well,
2:03
it’s it started with the fact that we had perhaps the largest defined contribution plan the United States on a per capita asset basis, because of the large amounts that you could put into these plans prior to the economic tax act of 1981 so we pretty much handled everything. And that time, lots of it was fixed income, because interest rates in the early 80s were quite high. But over time, we had very creative individuals at McKinsey, and they started looking at new asset classes, and we wanted to make sure that no one did anything crazy. So we got involved in that. And as we studied, we understood the very interesting value propositions and how to manage risk diversity, and so we started branching out to add a new level of investing as interest rates started post the mid 80s, to come back down and be less compelling as a return mechanism. So we pretty much did everything. And so my experience does go to a pretty broad range of investments perfect,
3:03
and then tell us a bit about the thesis and the investment approach at Spring Mountain
3:07
thesis. The main thesis is to look at things that we think are special situations, ie things that are undervalued to what they should be. And we’re asset class and sector agnostic, and we’re pretty we put pretty much roam around, looking for things that are interesting, that provide returns, that are if you were drawing a risk return graph north of that line, in terms of trade off in the risk and return, and provide some compelling, what you would call alpha generation capabilities. And that permeates all the products we have. We have several lines of business, if you like, from growth equity investing to a replication of the special situations portfolio, which became the main act at the McKinsey Investment Office, to muni bond investing and fund to funds. You just give us exposure to a very large range of activities. Perfect.
4:04
I’d love to talk a bit about a and you know, I’m I’d imagine you’re bringing a lot of your strategic chops from McKinsey, looking at various sectors and verticals. Curious to hear your take. Maybe just to start off here, you know, which sectors do you think are positioned best to leverage the advantages of of ai,
4:27
ai, I think has has very, very broad applicability and the power to really move things forward in a variety of sectors. And so you get to the specific in the area the the we at Spring Mountain, have one rule, we only invest in things that we know something about, have good networks and also spot the unrecognized value. And we like to do deep waves and and just as a result of a variety of things. Things we’ve become very close to the life sciences industry, or life science sector, particularly in therapies, as well as development of therapies. And so I think AI is particularly interesting, at least given our background in that area, not only on the healthcare side, and informing practitioners. And give you examples of how AI would be very helpful, but also to drug development and development, both therapies and treatments, in addressing our most complicated diseases, very good.
5:33
And then on the other side, which sectors or verticals do you think are poorly positioned to benefit near term from AI, and why?
5:41
You know, that’s very, very interesting. That’s the first time been asked that question as to what’s poorly developed, partly because I don’t focus on what’s poorly developed. We’re very, very proactive, and we construct things that we think are interesting. So I don’t know that I have an informed answer to your your question as to what areas are poorly developed somebody else with more might find the golden element of what I’m missing, and that’s a whole theory of special situations. What is the rest of the world missing because they don’t have or bring that particular insight or vision into your particular area?
6:20
Yeah, it’s an interesting question, you know, as we look through technology waves, you know, the adoption of software, broadly speaking, ASPs and SaaS later, adoption of cloud, right? I still think there’s, what, 48% of software that’s on prem, which is kind of shocking, you know, in today’s day and age. And then to think about AI and adoption of AI, you know, there will be some laggers, there will be some legacy industries. There will be some that, much like software or much like Cloud, are late to the game. And there will be some industries that leapfrog right, some that may have not adopted software or cloud, but AI is better positioned to kind of help them advance. We even see that on a country by country basis, right? Like countries like India can make large leaps forward because they’re not dependent on infrastructure and inertia that exists maybe in the States. So it’s interesting, but yeah, I will, yeah.
7:20
Let me give you an example of something that, you know, I said earlier that I think AI has almost universal applicability. That’s why your question was very interesting to me. And so one area that, well, okay, you know, how would you use it in waste, food waste, and today I received from my alma mater, Yale, a little bit about how AI is being used in managing food waste at Yale. And they have a scanner that takes a look at what is coming off of the backside of a restaurant, namely the preparation mode versus disposal, and identifies the quantity, quality, and then it informs the chefs how to minimize that waste and so forth. And so if you actually told me something that the mentality we have at Spring Mountain is, if you tell me I don’t know how to apply AI there, we might just go out and do it to find out, not to prove that you’re wrong. But something is we like like it. When the general population is missing something, they come to a conclusion without really examining it closely. Love
8:27
it. I love the example you gave. We, we have an investment in a company called food out of New York City, which is tangential. They work with groceries, and they they monitor, you know, all perishable food that’s sold so non barcode food. So think about the potato salad or the New York strip steak that you buy, and it’s computer vision. Like you said, it’s computer vision and a scale, and at the end of the day, they know exactly how much was sold. At the beginning of the day, they know exactly what their inventory is. They can reconcile that with the purchase order system and make sure that they’re not buying too many rib eyes, because about 45% of all that food is just thrown away in the grocery segment. So computer vision, lots of applications, lots of waste reduction, lots of benefit.
9:13
Exactly, you know, I’m
9:14
curious, Greg, to hear your take on the consulting services industry. Right? A lot of AI applications are either you know, either you know, benefiting consulting services in a variety of ways, and in some cases, you know it can replace a lot of time in in services that previously you know would be billed for. I’d love to get your take on whether this will be net deflationary to the TAM, you know, of the consulting services space, or if it will be, you know, consulting services will be a beneficiary, and tam will grow,
9:53
if they really think about it, I think it could be very additive to a consulting firm. Is why, if you take a look at the development of AI systems, you can develop it with people who actually help train those systems by asking it the right questions. One of the things that you skills that you hone as a consultant, you’re dumped into an atmosphere. You try to learn something. Everybody looks at what do you know about this thing, but you’re able to think out of the box and ask questions. And so if, if they sort of, instead of worry about it, say, Hey, listen, if you understand the true nature, we’re the best people to develop an AI system that would bring our insights even farther, deeper. Because let’s concede one part of it, which is, I think AI has potential across all these places, because it has the ability to process more information than we are able to do as individuals. And if we realize that, and we decided that, that these systems need to be trained and embrace it. The possibilities are, really, they have a better group to develop AI systems and therefore help their business that, that would be my hypotheses.
11:10
I think that’s fair. And you know, they’re seeing so many applications and use cases across so many different verticals and companies. There’s, there’s got to be advantages there in, you know, applying systems and new constructs and new applications of AI to their clients. Greg, you know, I’d love to hear more examples of, sort of the practical implementation of AI tech. You mentioned the waste management example before. You know. Can you give us some more examples of the real world problems that are being solved by applied? AI,
11:45
the simple example in the healthcare area, there are two factors that that are missing in terms of we’re going to rely on primary healthcare and a doctor, and as you go along, you like having that relationship, but new things discover, and so forth. And so, you know, you go to your general practitioner and you get a couple of tests to take your heart rate, blood pressure, temperature, readouts, you’re complaining about something they recall from their experience. The you know what these are symptoms of, and they identify the most likely and that’s what happens. But inside of that, there might be a time bomb that that we now know about, that we didn’t when they went to school and trained and so forth. And basically, if you add the real world data, the electronic health records, into the background, location also matters, and you give someone a locational reference, and then you put it to the system that practitioner would be so better armed to take a look at things and evaluate what your condition might be. Very simple example of a young fellow who died because his symptoms were not covid or, you know, related or something system was that it he was the there is a very rapidly moving cancer in the background, and something you didn’t search for because there’s no diagnostic you don’t do that first you address the symptoms. So I think an AI system would be very, very helpful, very, very impactful. And once you do it, the you don’t need consultation with 500 experts, you can just tap into the system. Very practical example of of AI in healthcare.
13:35
What you know? Why do you think, as we’re talking about healthcare now, I would like to transition more to kind of the biotech side and bioengineering side. Why do you think now is a great time to invest in companies focused on bioengineering? The
13:48
it’s a great time to invest, you know, because the there’s been a wave in several of the diseases that we’re interested in using what we knew. And then now, finally, getting to propositions and therapies, right? And that’s being gone through phase one, phase two testing. And so everyone’s concentrated on that. For instance, the A Beta theory in terms of Alzheimer’s plaques and tangles. And you might have heard of all that. And so there are several things addressing that. But in the meantime, there have been a whole class of tools, omics tools, single cell omics, spatial omics tools, which give us insights and higher resolution tools that actually give us insights to the other parts of the mechanisms that cause diseases. So we’re having a better understanding, from which we are now beginning to generate a variety of theories of how to solve for those problems before the they come down the pipeline. Because everyone’s concentrated in all of these things, and I know this is a gross generalization, there are lots of opportunities to gather in. Give life to those possibilities. Invest in them. I would submit you could almost get them cash free, because there’s so many of them lying around, develop them with a group in a more economic sense, and then see how things evolve. So I think there’s an opportunity, because there’s a whole new class people like to lag on to things that that seem to be further down the wrist line and more less distant. And the opportunity is starting early evaluation seems right and and so I’m very excited about new ideas coming down the pipeline, generated not through traditional theories, but generated with all the information that we are putting onto the pipeline, and that that is industry actually being spawned by biomedical engineers, and they’re understanding, okay, reducing, understand the mechanism of how these things occur. And then, then, so that’s the basic science, and then doing the translational medicine part at
16:01
Spring Mountain Capital. Have you conducted any incubation or studio efforts? You know, as you see opportunities and you’re connected with, you know, best in class teams, have you sort of sponsored and incubated any of these companies? Or are you more trying to meet, you know, founders, where they’re at and then make an investment. The the we have,
16:23
have not done it, but this is what we’re doing now. So in, you know, recently started because we’ve got the other parts. And one of the, I think, big wave areas is social impact investing. You create a social impact investing along the lines of a life sciences, social impact being democratizing the participation of individuals of color from socioeconomically disadvantaged communities in the process, and that brings in talent, passion, which very high in Our criteria, to solving a problem within a community. So we are now to realize that, okay, you’re bringing a whole new generation. Our model is not an incubation model, because, you know, it’s not good enough to just incubate. It’s nice, and so I’m not denigrating that effort. It’s great, but if you generate something that has impact, you should see it through. So in our project, which is called West Harlem Innovation Network, we’re more than incubator. We’re looking at a studio, model investor, studio, builder studio, on the in the builder studio, we will work with people who are writing some very interesting papers developing IP who are at research institutions and and you know, you find a lot of these individuals love to teach and continue developing, so why take them offline? So our proposition is, look, get those ideas, surround them by the team, and do more than incubate, form the company, nurture it, continue to provide funding to it, if it doesn’t turn out have a group of individuals that you can put within a platform. So we’ve gone beyond the incubation model and what we’ve built. We are we’re a ground floor studio. We do go through incubation. We’re protective of the people we bring in. Give this opportunity by having platform companies, because they do relate, and the skills are transportable, if one thing doesn’t come out, be close to the research institution, so that we don’t move this next generation of researchers who want to teach out of their environment. So that’s what we’re about. Love
18:42
it. So you talked about, you know, building platforms. And, you know, there’s this concept and venture of the platform paradox, right? If you try and build a platform out the gate, you’re solving a million things instead of one thing. And so a lot of those companies fail early on, whereas many of the notable tech companies that are platforms today actually started quite small, you know, with one application, killer app with a wedge. So, you know, you spend a lot of time at Spring Mountain assessing this platform potential of investment. How do you avoid the trap of the platform paradox?
19:17
If you put your investment hat on, which talks about the risk of inventing one thing versus multiple things. And you also see, as you consult whether you do a platform or whether you have best in class of one thing, and then branch out and subsume other things, when you take a look at the cost of acquiring the ideas that it’s, as I said, nil because nobody’s really paying attention to them, once again, a generalization. But secondly, the skill set of moving them along, which is you’re trying to take an idea and get it into clinical testing, doing the preclinical and so forth. All same skill set, the there’s a fair amount of grant money for each class, and so you can actually do it very economically, much more economically. Than particularly if you have related ideas. So conceptually, when we talk about platform, it’s not a scattershot it is ideas that are related. You got to understand, take Alzheimer’s, Alzheimer’s result of a variety of breakdowns caused by aging or poor management of health. So there are all these related plays. And so when we say platform, they’re not just a scatter short, they are well rationalized pieces in a very complex puzzle that we’re trying to assemble. Now against that we look at other technologies that are coming along. For example, if we talk about brain diseases, I think very interesting is electrical stimulation and implants and so forth. So if we succeed in growing, say, new neurons, the that you know one looks at, well, why would you do that when this is coming along? Well, actually, you grow some new neurons, the electrical stimulation might actually be very complementary, or both may be complementary to each other. So we take a look at the synergies with that, which is the proposition and platform comes not scattershot thing of a bunch of things you put in the platform, but you got to really think about those synergies. That’s why, when we construct our advisory boards, we look at people. We’re not all the same, same mechanism, action, same technique, but who have a global view of the diseases that that we’re addressing Perfect?
21:29
Well, if you come up with a reliable method to grow more neurons, please send it my way, Greg, I would, I would love to have access you. And I have had some dialog.
21:38
I used to mention we actually have one is very illustrative of what what we’re working on. We we have one that did, in fact, grow new neurons, all right? And the because of underfunding, which is a problem, you didn’t get proof of concept in terms of somebody actually moving. We were addressing chronic spinal cord injury. Yep, right. People who have been injured 10 years ago regrow the neurons and get them to move. Right now, we grew the neurons, but because we underpowered the study, and nobody really moved, and because we couldn’t test long enough, and we put it in Stacy’s, and I think the government is going to go fund the phase two. And if they do then, then we will try to solve for so we’re going to hold on to and keep it an additional life. But you can grow a neurons. You can regrow the spinal cord. You can regrow the peripheral nerve, which is why we’re going to take this company out. And the shame of it all is it might actually work, but it might not see the light of date, because the it’s so expensive to develop these things here, so we’d like to be able
22:51
to house it well. Hopefully the government comes in and makes that
22:54
happen. So I will call you short. Sounds good,
22:57
if I had enough cash, you know, maybe we could do something. But anyway, you know, Greg, you and I have had some back and forth on quantum computing. I would love to get your take here. You know, if I’m wearing my bear hat, you know, it feels a little bit like cold fusion. It’s a hype concept that’s been years away from fruition for decades. How far are we from true quantum computing, and why is now the time? You
23:22
know, interestingly, that question was asked by a major investor to a close friend of mine, and he did a very good assessment, and about five years ago, he told me, at least 10 years it may be closer, but that’s only step one. Quantum Computing, you then have to figure out how to program but it’s coming. And given the length of time that these things are taking, and the amount of assets being thrown into it because of its importance to really unleashing the power to AI and the massive amounts of money going in there, I think it’s sooner than you expect quantum computing. So I would pay attention to all of the centers that I think have have something that might make sense. And then when you go to these places, their expectation is they realize the second leg of the problem. So you take a look at their actions, and they are actually trying to develop ways to program for quantum computing. Now, I think, with the creativity and the talent, if it’s supplemented by money, these things will happen sooner rather than later even CRISPR, a very good friend of mine in the field, when five years ago, he said, No, we’re a long way from anything coming out of CRISPR, the crystal balls are not always accurate. Well, crystal balls take assumption of with with the money at hand and the interest at this. Of this what happens? And lots of people realize this. It’s sort of in Alzheimer’s. It was dead in the water until the FDA realized we got to give some hope to people, and so they approved something, even though the data was not all there, and they got heavily criticized for it, and then all of a sudden you have two things that FDA approved and still not there, but it awakens the entire industry, and 15 things come back up on onto the data. People. So when you look at how these things come together and the talent in the human population, I think it happens sooner than you think. And I think we’re we’re going to pay attention to it and and make sure that we get in in early on someplace, and we have a piece of the equation.
25:18
I mean, it feels like the type of technology shift that can be as transformational as AI. I know these two things go hand in hand, but it feels like, you know, if we achieve ubiquitous quantum computing capabilities, it will fundamentally change, you know, many of the major incumbents in the way things are done. I’m curious to get your take on whether Nvidia, Google, AWS, Microsoft, mag, seven companies will maintain their dominance in a future with potentially ubiquitous and democratized quantum computing.
25:56
When I look at AI, it’s not about AI. AI is simply a tool. I think in order to have dominance, it’s not the best algorithm, because there’s no really best algorithm. Why? Because more and more data is coming online. The algorithm you write today, which may be the best algorithm, will not be the best algorithm tomorrow. So what are the really base elements that you have to control? And I know people might differ from this, but we believe it’s data. You have to have data and proprietary data, to the extent that you can have proprietary data, right? And then after that, more data. Then you need quantum computing, and then you you get some genius scientists to write programs that go beyond linear correlations and reversions and everything else. You go to individuals who can write stuff in four dimensions at least, and then interpret the world in unseen dimensions. The other stuff is just cranking numbers. But Can Can you actually construct our understanding of what reality is in more than the devices we can observe. For instance, we can’t see a black hole, but we know it’s there, and we know it has real impact. It can move planets. There are things in in our body because of complex relationships that are occurring that you can’t see. And those are mathematical constructs. That’s where AI becomes really very interesting, the construct enabling us to reinterpret reality, very interesting. Books on this in terms of what we see and call reality is as a mathematical construct, several individuals have written about about this, therefore more data, better mathematical contracts, we may really be able to see reality and understand solutions. That’s the really exciting part in front of us.
27:46
It is exciting. I mean, if we have the computing power, and the cost is, you know, de minimis over time, and you know, we have the ability to train whatever models need to be trained. I guess the limiting factor becomes the data set right in collection. The
28:07
limiting factor is actually your ability to capture the data set. Yes, give you a simple example. We have a tool that in spatial omics, developed by a biomedical engineer that we know very, very well, chair professor at a major university, and it was far ahead of anybody else’s spatial omics research tool. We could put 100 times more data than a billion dollar company, well known billion dollar company, in the space, and that was when we first took over the idea. Three years ago, three years later, we can generate 1000 times more information, which means 1000 times, 100 times is 10,000 more, 100,000 times more pieces of information from a single cell. Now you get a collection of cell the multiplication, and then you add in the the other factors, there will be more data on it. Data is not concerned proprietary data, however, is, is what if you can now generate a data set that only you have, because there are multiple ways to solve math problems and and basically, I wouldn’t, there’s certainly not dime doesn’t, but nevertheless, very hard to patent an edge in in computing. So you do see some of the AI companies talking to companies like our Research Tool Company, as to whether or not they can have access to the proprietary data that we generate. Greg, what
29:27
are your thoughts and what’s the firm’s approach to Aging and Longevity? We
29:33
treat them separately. Aging definitely within our group, and it varies, is all those efforts or treatment of aging. As you get older, your systems break down, and so we try to ameliorate the effects of it or slow them down. But when we work in our community and when we address therefore in in West Harlem Innovation Network, the companies we will fund are companies that. That we believe could improve the health and educational outcomes of underrepresented minorities, true, we’d like to do it for everybody, but we have a focus on something that’s been neglected, which is understanding the underrepresented minority, particularly those who are in socioeconomically disadvantaged communities. Remember earlier I said it’s location as well as personal characteristics. Okay, so when we take a look at at that, you see that there are race based differences in individuals, but you see over time that there are also environmentally cause changes individuals, for example, the increase in breast cancer in black women, right? Very interesting thing that American Cancer Society put out. There is no difference between mortality rate between black and white women back in 1960 but scroll ahead 60 years later, with all the improvements, something is wrong here, because there is now a very big differential, and then the same with Alzheimer’s, and that emanates not only from race based differences, but how we treat our children. For example, what we feed them, poor community food program, highly processed foods, which we know do a variety of things in terms of impacting life and longevity. So we are very, very concerned about that. We’re concerned that life expectancies have increased, but the percentage of your life, even longer life that you spend in good health versus poor and moderate health, is still 5050, now the aspiration is that we decrease that, but it’s not going to happen for underrepresented minorities and socioeconomically disadvantaged communities unless somebody gets their data and puts it onto this information highway that we’re doing. So that’s what we’re focused on in what we’re doing. And we and we think it’ll be big, because I think there will be a realization in America at least, that this is very important, and people who do it should be funded, and because they’ll save a lot of downstream costs, and it’s important to do. So that’s what what we’re looking at. Yeah,
32:05
we’ll change demographics quite a bit if you’re successful in that endeavor. Greg, you launched a new social impact strategy, W, H, I, N, I assume that’s pronounced when you know, why did you format and what advice do you have for other investors that are looking to create social impact through their investments?
32:27
We started with a challenge. As I alluded to earlier, you asked. He told me, something won’t work here. I might take a look at it. Friend of mine was looking at the opportunity zone legislation. Asked if we were going to do something with it. And I said, No, it’s it. It, you know, someone very interested in urban development, just side curiosity. It gentrifies areas because it emphasizes buildings and development and so forth, and and so he said, Well, can it be applied to like people and companies? So as I said, I was a tax lawyer, I looked at the legislation, and I said, you know, it actually can be, and if they draft the right regulations, we can develop people and companies instead of buildings and real estate. And as we look further, we said, what? What is a proposition that would really engage people who are suspicious of anybody’s motivation to come into their community and and we found out, not found out. We just started seeing the numbers in terms of the feedback effects of lack of participation in life sciences, both in terms of jobs, opportunities and so forth. And we said, this is more important, and with our prediction of where the money is going, why just do an opportunity zone fund? Let’s open a social impact investing wing. And I think, I think it’ll be a very, very investable area. A couple things need to change, and you reach a point in life where it becomes very important to do something for your community. Various people have said you don’t really get fulfilled, right, unless you do something for your community. Right? You can be rich have a high number on your balance sheet, but you’re not wealthy unless you do something for your community, and so now it’s not about me. I find that that that is very inspirational to our current generation, and for a variety of reasons, I like working with this generation, because it’s for some reason, we’re set up to have the next greatest generation, and and having something that motivates them gets them off of all the other issues, really. So the last leg is just the satisfaction of of taking talent and shaping it properly, and to get to the end point of what one’s life should be is, you know, just just invigorating. Yeah, and helps with my longevity. And
35:02
advice for other firms that are looking to do similar. You know,
35:06
you you the yes, do not compromise. If, if you are, if you really want to move social impact investing right, challenge yourself to if it’s investing, you have to give a proposition to the investor, and it’s vitally important that in all these problems, we enlist the private sector. I find that go to a lot of things on sustainability, and you’re talking about government and not for profits, and you don’t have the participation of private wealth, which is substantial, which might be totally motivated. I go to social impact investing, and they say, Well, what’s your measure of success? Oh, I’m going to be top quartile performance now. What’s your measure what I’m doing, what we’re doing in West Harlem Innovation Network. And by the way, we use West Harlem because our companies will be in the community. Very important. You want to solve healthcare for underrepresented minorities, you got to start with people from the community, and you got to be in the community and establish that trust. Trust is an element. So we said, look, you want to move the needle on economic development. You want to move the needle on underrepresented minorities having opportunities. And you want to do it here. You form structures that make sure that they get paid. You provide a return to your investors, and they keep on. So our motto is, we want to do distinctively well by doing distinctive good. So my advice and short, long answer your question is challenge yourself, if you’re trying to do an investment, to forward the proposition that this is an asset class, it’s investable, and enable people to draw on that. It’s not solvable completely. But then hopefully institutions like our and the wealthy individuals we have will donate to, not for profits, but if you’re going to enter social impact investing and really want to move it forward, challenge yourself to find the investable proposition what you’re doing, Greg,
37:14
if we could feature anyone here on the show, who do you think we should interview, and what topic would you like to hear them speak about,
37:20
you know, relevant to what I said, depends on what you want to hear. But, but one person whose book I just read, who I think could would be very compelling and inspirational. You know, I just read Mike milken’s book, which he he finished at the end of the pandemic, or published titled Faster Cures, the it’s there’s there’s wrapped up into that book. A lot about about his background and topics, I touch on perspective and so forth, and what we’re doing in win. Fact, when someone looked at it, he said, Bob, did you read that? And then design win? And the actual fact is, no, it’s right there in front of you. And then to find someone who is aligned. I do that because it’s so refreshing to find someone that might is probably ahead of you in the thinking, but is so aligned to make you feel and that’s credibility because of his perspective and talent, prior success, to do that. So, you know, certainly Mike Milken, and he has normal things, and so it’s not it’s easy to do that. I love futurists in terms of thinking about the nature of humanity and where it’s going. But I like topics of data. Jordan Metzl, who just wrote the book, super convergence. Or to speak, I’d certainly go and listen to Jordan Metzl speak. So just in terms of what I’m involved in, and if you want to go follow up those two guys would would be pretty, pretty interesting.
38:55
Greg, do you have any habits, tactics or behaviors that are a force multiplier
39:00
somewhere? I found that I could function on basically five hours of sleep a night. Wow, the that gives me three hours extra over a person with eight hours of sleep, and multiply it over a number of years, that gives me, you know, maybe 1020, more years of productive, living, productive, awake time. So that’s fine. Now we know, if you read books, I would read Peter Attias book on outlive. He says, Look, you got to get sleep. You really have to get sleep. And I’m trying to do that. But exercise a big function. And so the the I think there is an interaction between keeping healthy without that by exercising also maximizes my time now that there are audio books and everything else and so my habits are which is a multiplier be healthy enough to add three more hours of wait time. That’s not a Universal Recommendation. Some people can function on that. I seem to be. Able to do that, and that is in my spirit, which is why do something just because somebody tells you to do it, find out how it applies to you, right? All these rules of eating well, staying healthy, reducing stress, there’s no way I can reduce stress in my life. Just, you know, doing high return stuff is stressful, right? So I’m not going to do that. And so you got to figure out, well, which things would you give back that will create force model, and it’s all individual. There’s no universal prescription, because we are all different.
40:34
And finally, here, Greg, what is the best way for listeners to connect with you and follow along with Spring Mountain Capital? Let’s
40:40
see right now, get onto the LinkedIn account. I’m going to be more, a little more active posting what I’m doing. I don’t think I will be blogging and writing to people, but if you’re interested in what we’re doing, go on to that. You can visit our website, Spring Mountain, capital.com, or you can go to win dot NYC. As we begin to populate what we’re doing in win, we’ll put in publications and things we talked about, which, you know, for example, I panel I participate in recently on healthy aging and communities of color, and my input on on how to do that, and we’re going to try to expand on that, because we think not only is it important, but the formulation of how we’re doing in it, doing it is very important. We’ll enrich a community, enrich people of color, who will hopefully be grown to be responsible to their communities with the wealth we generate and encourage investors to invest in what we’re doing.
41:49
He is Gregory ho the firm is Spring Mountain Capital, Greg thanks so much for the time today. And you know, I really applaud all that you’re doing on the social impact front. So thank you, sir.
41:59
Well, thank you very much. Really, really appreciate your taking an interest, and really appreciate your comments.
42:11
All right, that’ll wrap up today’s interview. If you enjoyed the episode or a previous one, let the guest know about it. Share your thoughts on social or shoot them an email, let them know what particularly resonated with you. I can’t tell you how much I appreciate that some of the smartest folks in venture are willing to take the time and share their insights with us. If you feel the same, a compliment goes a long way. Okay, that’s a wrap for today. Until next time, remember to over prepare, choose carefully and invest confidently. Thanks so much for listening.
