AI, Jobs, and What Comes Next with Kumar Garg of Renaissance Philanthropy

Kumar Garg is the President of Renaissance Philanthropy, where he leads thesis-driven philanthropic funds focused on major global challenges. Previously, he worked in the Obama White House Office of Science and Technology Policy and helped build Eric Schmidt’s science and tech initiatives.

💡 5 Things You’ll Learn in This Episode:

1.  How AI is projected to impact jobs, with forecasts suggesting modest but meaningful workforce contraction 
2.  Why community colleges and skilled trades may see rising demand in an AI-driven economy 
3.  How “superforecasters” are being used to predict labor market shifts more systematically 
4.  What policymakers and educators should track to prepare for AI-driven disruption 
5.  How Renaissance Philanthropy is rethinking how capital flows into big, thesis-driven ideas 

✨ Episode Highlights:
[00:02:34] The origin of Renaissance Philanthropy and its thesis-driven funding model
[00:07:00] Why Kumar Garg built the Labor Automation Forecast Hub
[00:10:30] The role of super forecasters in predicting AI’s impact on jobs
[00:13:00] Key projections: job displacement, slower growth, and shifting career paths
[00:15:30] Community colleges vs. four-year degrees in the AI economy
[00:17:45] Why the future of work is a “big dislocation,” not a collapse
[00:21:00] How forecasts evolve and why trend direction matters more than exact predictions
[00:23:30] Reactions from policymakers and what data matters most
[00:26:18] The growing importance of regional workforce forecasting
[00:26:49] Kumar’s outlook on the future of philanthropy and deploying capital at scale

😎 Stay updated with Edtech Insiders! 

Follow us on our podcast, newsletter & LinkedIn here.

🎉 Presenting Sponsor/s:

Every year, K-12 districts and higher ed institutions spend over half a trillion dollars—but most sales teams miss the signals. Starbridge tracks early signs like board minutes, budget drafts, and strategic plans, then helps you turn them into personalized outreach—fast. Win the deal before it hits the RFP stage. That’s how top edtech teams stay ahead.

Tuck Advisors was founded by entrepreneurs who built and sold their own companies, frustrated by other m and a firms, they created the one they wished they could have hired but couldn't find. One who understands what matters to founders and whose North Star KPI is the percentage of deals closed. If you're thinking of selling your ed tech company or buying one contact Tuck advisors now.

Cooley LLP is the go-to law firm for education and edtech innovators, offering industry-informed counsel across the 'pre-K to gray' spectrum. With a multidisciplinary approach and a powerful edtech ecosystem, Cooley helps shape the future of education.

Innovation in preK to gray learning is powered by exceptional people. For over 15 years, EdTech companies of all sizes and stages have trusted HireEducation to find the talent that drives impact. When specific skills and experiences are mission-critical, HireEducation is a partner that delivers. Offering permanent, fractional, and executive recruitment, HireEducation knows the go-to-market talent you need. Learn more at HireEdu.com.

Transcript

[00:00:00] Alex Sarlin: Tuck Advisors was founded by entrepreneurs who built and sold their own companies, frustrated by other m and a firms. They created the one they wished they could have hired but couldn't find. One who understands what matters to founders and whose North Star KPI is the percentage of deals closed. If you're thinking of selling your ed tech company or buying one contact Tuck advisors, now. 

[00:00:24] Kumar Garg: What you actually find is that this very specific trends.

That are already showing up in the economy, but they're gonna keep going. And so the policymaker point is that we have to actually grapple with it. If you're a governor and you're not playing out these questions and saying, how is this gonna, are we investing enough in the institutions? They're gonna create the kind of graduates, they're gonna get the jobs.

Are we thinking enough about job transition? Again, these are projections. They're not crystal balls into the future. It sort of raises the question of, do you have a strategy?

[00:01:00] Alex Sarlin: Welcome to EdTech Insiders, the top podcast covering the education technology industry from funding rounds to impact to AI developments across early childhood K 12 higher ed and work. You'll find it all here at EdTech Insiders. 

[00:01:15] Ben Kornell: Remember to subscribe to the pod, check out our newsletter, and also our event calendar and to go

deeper check out EdTech Insiders Plus where you can get premium content access to our WhatsApp channel, early access to events and back channel insights from Alex and Ben. Hope you enjoyed today's pod.

Hello, EdTech Insider listeners. It is really an honor and privilege to have a good friend on the podcast. Kumar Garg, President of Renaissance Philanthropy. Kumar has helped to shape the science and tech landscape for almost two decades. Working with Eric Schmidt, he helped design and launch moonshot initiatives in education, provided early support to game changing ideas and pioneers, and built ongoing multi-donor and multi-sector collaboratives.

And we're here to celebrate the two year birthday of his latest enterprise, which. Is Renaissance Philanthropy? Happy two year birthday Ren. Phil. 

[00:02:15] Kumar Garg: Thank you. Thank you. It's been quite the run. I think we were like, what? Where will we be in two years? But we've been growing and lots of exciting work. 

[00:02:22] Ben Kornell: Yeah.

Before we dive too far in on the Labor Automation Forecast hub, which we really want to cover for our listeners, stepping back, just tell us a little bit about what Ren. Phil. is and how it even started. 

[00:02:34] Kumar Garg: Well, it's great to be here. I'm an avid listener. My background started in policy. I was worked for President Obama on science in his science office, and so I got a chance to see r and d policy across a range of different topics from education to other areas.

And one of the things I was really interested in is. What is the future of education r and d? And then when I went afterwards to work for Eric Schmidt, I helped build out his science and tech foundation. I kept pulling on that thread. So we actually designed a number of moonshot RD programs. So our first learning science meets AI RD program that we designed actually launched in 2019, so three years before ChatGPT

At the time, I remember doing one of those big donor meetings where we invited a bunch of our peers and one of them said, you know, this is all very interesting, but. It's kind of niche. So I think the world caught up to the importance of thinking about what are the public goods. That can actually drive AI progress.

And then what the impetus behind creating Renaissance was. One of the things I noticed when I was working for Eric was just that he had a lot of peers and a lot of those peers were interested in embedding on scientific technology in a big way, but they did not necessarily want to build a foundation. A large team that they were directly managing, and often they would feel very stuck with their options, which is maybe they would get somebody like me on the phone and I would tell them a couple of things that we were thinking about, and then they would add a little bit on top.

Or maybe they would write a large check to the alma mater. But then the big. Option that they would be given it just like decide later, you know, once you're retired, once your kids are grown, then you can get going. And I don't know about you, but I think the world is full of huge opportunities and later doesn't seem like a great option.

And so the idea behind Renaissance was, can we borrow an idea from the investment world? In the investment world, you have a number of organizations. They basically build funds. They build investment funds with a thesis, and they raise money from wealth owners as LPs could we do that in philanthropy? So we design thesis driven philanthropic funds.

Each one has like a three to five year thesis behind it. It has a technical leader that leads it, it has a team attached to it, and then it goes out to donors. And rather than. Raising money at individual projects, it raises money at the thesis level. And so it allows these donors to then potentially bet much larger amounts of resources against a particular thesis that's important.

And so we do that in ai, in energy and climate, in global development, in human health and in education and economic mobility. And so the nice thing about when I first started this two years ago. I got a lot of feedback that donors are obsessed with control with credit. This is just not the way things would go, but we've been finding a lot of partners in progress.

In fact, betting on what we're hearing, both from established donors and new ones, is that they wanna bet big on science and technology, but they wanna do it with experts and on a thesis where they think you can make credible traction. And so. Yeah, we're gonna release a report soon with a lot of our results from our second year, but have had a good amount of momentum and been able to move a lot more capital than we expected for the two year mark.

[00:05:41] Ben Kornell: Yeah, it's so interesting hearing you explain it too, because part of the value proposition of philanthropic infrastructure as a service is really compelling. It's like all of this setting up of funds and managing it and making sure you're compliant with all of the legal components. Is a huge barrier to entry, but then to go the step further and actually make them thesis driven, I think has been so compelling.

And for many of us, we see the DAF assets ballooning, where there's all of this potential philanthropic capital sitting on the sidelines. And so really creating the mechanisms not only to structure it, but to back world changing ideas or world changing feces, I think is such a great opportunity. Frankly, when you first started launching it, I was like, I'm not sure I get it.

Will this work? And here you are at the two year anniversary and the flywheels just starting to kick over and. Just been so amazing to watch. In addition to doing this work, you're also, you've invested in thought leadership, and one of the things we'd love to hear a little bit more about is the labor automation forecast hub.

So start at the beginning. What inspired the creation of this forecast hub and what specific gaps in the conversation around AI automation in the future of work were you aiming to address. 

[00:07:00] Kumar Garg: Sure. I think, as I said, our general goal is to build these multi-year thesis driven funds, and one of the topics that comes up a lot when I talk to donors, when I talk to policy makers, when I talk kitchen table, is this the intersection between AI and labor.

It's this big debate that we're having around as AI progress accelerates, what effects will it have on the labor force? What skills do we need? Will it lead to labor displacement? I think one of the views that we were exploring as we started to think about whether we should have a thesis driven fund in this area is actually that there's just a lot.

Energy and conversation, but our tools for actually understanding where everything is going haven't substantially improved. And by that what I mean is that, I dunno about you, but I feel like we ricochet between this. CEO said this and maybe Silicon Valley is no longer hiring entry-level engineers to this report came out from this economist and they did a careful.

Skills breakdown of these jobs and these 14% of jobs are about to disappear. And there's this constant next new input and debate. And one of the ideas that at least I was very interested in was just. Are there disciplines that actually think about scenario planning, forecasting amidst uncertainty because it's not that we can actually see the future.

And so what are ways that we actually can reason about the future systematically? And one body of work that I was personally very interested in is. There's a academic Phil Tetlock, who has done a lot of core academic work on forecasting, and he built out this field one brick at a time. But one of the things that he actually realized through specific experiments is that some people are better at forecasting than others, as in they're able to take a lot of disparate data, and then if you tell them to reason about whether some event will happen in the world.

They can actually systematically do better than throwing a dart on a board or just an average person on predicting that future outcome. And he had a series of insights he's like worth reading. For example, these super forecasters often will out compete experts in a field, which is mind blowing like. You know a ton about this particular area and this person who's a generalist with the same data.

And part of that is the expert bias, which is sometimes we have a worldview that develops once we have expertise, and then we discard information that violate that worldview. But one of the things that the Tetlock work led iarpa in the US government then started to do forecasting tournaments of actually bringing these super forecasters together and having them predict world events.

So this has really taken off so. Now we have a whole world of prediction markets and everything else, but one of the things that I was really interested in is forecasting has continued to develop and there are platforms now like Meticulous, which are actually communities of super forecasters. So you listening to this podcast right now can go sign up for a me account and just start making reasoned forecast about events.

And what meticulous does is that it tracks your forecasts and just sees over time how well you're doing. There are people in the meticulous community that just, they attain super forecaster status. They've just been at this again and again and been consistently above everyone else on the platform. So the conversation I started to have with meticulous, and this happened due to support from the Schultz Family Foundation, is what if we asked this community of super forecasters who are good at predicting other events about systematically reasoning about what AI is going to do.

To labor and the job market. And again, it's not that they have access to secret information, it's just, it would be interesting to see how they reason about these questions. And the nice thing about forecasting models. Is that you actually asked the super forecasters to write down their reasons. What was the thought process that led you to this conclusion?

And you can actually update. So if new data comes out and a forecast changes, what caused it to change? So I find that to be a very important way that we are gonna navigate this moment. Because if you're a governor, if you're a mayor, if you are a school leader, if you're a parent, you don't want just the answer, okay?

This is what's gonna happen to jobs. In six months or in a year, because who knows what you want is like, what are the things I should be tracking in the world that if they changed my estimate as to what's gonna happen to this area of jobs might change. And so that was the basic idea behind the forecasting hub.

We launched a competition on me about six weeks ago. We earlier the, just a few days ago. Announce the first set of forecasting results. But this is actually a live challenge on the Metech platform. So if you're listening and you wanna add your forecast, you can sign up and add in your entry. You can certainly consume it and see what the forecasts are right now, and these forecasts will change as more people participate.

As more data comes out. But one of the things we wanna do is start a conversation with policy makers and decision makers and others to say, what are the most important questions that are keeping you up at night? So those can start to show up in the hub, but also, I'm also hoping that this, we can go to folks who are data owners.

You know, folks in the companies who are thinking about AI adoption, other players who can then say, oh, we actually have data that we would love to make more visible to the forecasting community because it should be part of the analysis. So I'm hoping it helps on both sides of the market decision makers and on folks who might have valuable data that could help sharpen these insights over time.

[00:12:37] Ben Kornell: Yeah, anytime you're dealing with forecasting, there's all of this concern about the variability of outcomes, but actually one thing that I really appreciated about it is it sparks a conversation whether you agree with the numbers, whether you agree with the projected trend or not. It's forcing you to ask questions like, which jobs are most vulnerable to automation?

Which jobs aren't? And what's likely to happen to those sectors. At what rate? And I think the benefit of these forecasting platforms too is that as new data becomes available, it's ingested into the forecast. So it's not a one shot deal, but essentially becomes a living resource for folks. With that caveat in mind, your initial drop showed that overall employment is projected to fall by 1.9% by 2030.

3.4% by 2035 due to AI driven displacement. And that is a big contrast with government baselines of 3.1% growth over the next decade, especially when accounting for aging adjusted population trends. It also projects a leaner Fortune 500. The idea being that these large companies can actually operate with a much.

Smaller employee footprint that probably can be extrapolated across the board. And then new realities for the next generation. And this is where our EdTech listeners are keenly interested the idea of a four year degree as the safest path. Well, there's questions in your forecast that. It shows unemployment for new four year college graduates projected to more than double to 12% by 2035.

Meanwhile, degrees awarded by trade schools and community colleges are forecast to increase 26 by 2035. Now, these are all forecasts, but as you look at that first glimpse of the future from this forecasting, what are the key takeaways you think for policy makers? For education leaders, for entrepreneurs, where are the opportunities?

And also maybe, where do you think were the biggest surprises in this first data drop? 

[00:14:45] Kumar Garg: I think what's interesting is. I mean, let's start with the composition of firms. I think that the US has pretty healthy turnover on which firms end up being in the Fortune 500, the 500 most well capitalized firms. So compared to other countries, we do have quite a bit of turnover, but some of our largest firms are also really large employers.

I think a change in which you can have really high capitalized firms. But that actually have really small employee bases, I think changes potentially what end up being the employer mix. Now, most net new employment in the US does happen through what are called new businesses. So startups and net new businesses have driven a ton of employment in the us but on that large employers end up capturing a lot of the employee share.

So I think. That has implications for when you're coming outta college or when you're coming out of job transition, whether you should be starting your own business, you should be thinking about startups. What is the chance that you're gonna end up long-term at a single company? I mean, these are long-term trends, but it sort of suggests that potentially those trends could continue.

I think the community college versus four year, I mean this has been playing out over the past decade. I think one thing that's sort of interesting is the distinction between community colleges is four year. What some of the forecasters I think are looking at is just. How much of a demand in this kind of AI boom there is for the AI build out.

You know, you need lots of trades folks and other folks to sort of fuel the build out. And so whether it's building more homes or building more data centers or building more of the energy grid, there's just like a huge amount of infrastructure work that is gonna be needed and the sort of role community colleges can play.

And versus I think. There's gonna be some degree of pressure on certain types of white collar work. And so I think these are like generally ideas that are already getting debated in this AI discourse. But what's interesting is just how that might play out around which schools benefit and where students might end up.

I also think like. One interesting thing is that it does mean like net employment going down, I think has debates on how we debate immigration into the country. It also has a question around, we're not talking about. A prediction that like no one has a job, right? We're talking about a displacement that's, you know, within the bounds.

But if an economist told you that net employment was gonna drop by 4%, that's a big deal. So I think we both have to take it seriously, but it's also, these projections sort of suggest that it's. A big dislocation by economic terms, but not like, you know, I shouldn't go get trained. There's not gonna be any jobs in the future.

So I think that one of the things that we're gonna just push on is continuing to sort of double down on the sub components of this data, but it sort of suggests that one thing I was sort of curious about was, we're super forecasters when you're given this data, were they're gonna just go super against consensus.

Like, oh, actually AI is a total fad and nothing is gonna change. Well, they didn't say that. And then also they did not go the maximalist route and say, oh, you know, in the next 10 years, work is gonna disappear. What you actually find is that this very specific trends. That are already showing up in the economy, but they're gonna keep going.

And so the policy maker point is that we have to actually grapple with it. If you're a governor and you're not playing out these questions and saying, how's this gonna, are we investing enough in the institutions that are gonna create the kind of graduates, they're gonna get the jobs? Are we thinking enough about job transition?

Again, these are projections, they're not crystal balls into the future, but it sort of raises the question of do you have a strategy? 

[00:18:28] Ben Kornell: Yeah, and these are things that have, you can apply your own confidence interval on the range of outcomes, but if you're a school system leader or a policymaker, you've gotta be thinking about these 10 or 20 year trends and they, you know, the scenario planning that ensues from these types of projections help you figure out upside, mid case downside.

My initial reaction to some of the data was that it underappreciated globalization of the workforce and offshoring as a potential. Just having seen like call centers basically decimated globally. What do the folks do in offshore call center? Countries, they all get a subscription to chat GBT or Quad, and then they uplevel their kind of customer support into a job that was previously only onshore because of English proficiency or sophistication.

So there's a way in which I think this is the challenge if you're US centered, is you've gotta also take into account the multiple variables of what are other countries going to do. And how will that affect work? 

[00:19:38] Kumar Garg: Yeah. I am curious. I mean, one of the things again that I'm sort of curious around is what new data or what new like.

You know, employment trends or international trends cause some of these forecasts to shift substantially. 

[00:19:56] Ben Kornell: Where's the sensitivity in the 

[00:19:57] Kumar Garg: forecast? Exactly. So, because a common forecast is like, when is a GI gonna happen? You know, like when are we gonna hit this capability of when able to do graduate level work and the following 10 disciplines or you know, some marker.

'cause obviously you have to define it. And what's interesting is on a lot of these forecasting platforms. The prediction used to be like, it won't happen before 2050, and then it became like 2045 and then 2040, and then because there'd be a range among the forecasts, but then the consensus, the modal number kept getting earlier and earlier.

That doesn't necessarily tell you like exactly when it's gonna happen, but what it's telling you is, you know, at key points when new data came out, a new model came out or something else happened. On net, all the super forecasters shifted their timeline's up. And so I also think like the net change in forecast is its own interesting signal.

So let's say the forecast we put out today on the hub, if you know in six months those forecasts are getting more negative. That trend line is sort of interesting that like, oh, what are we seeing more of that is causing everyone to update in a certain direction or new information has come out, but it's, you know, it's noisy and therefore the contestant doesn't change.

Or actually it goes in the other direction. And so for me, like it's also interesting to watch, you know, as we keep seeing more turns. Of the cards, like how does that change the sense of the direction the forecasts are going? 

[00:21:30] Ben Kornell: Yeah, I mean, it's interesting as you built your fund modeling what financial investment funds do, this is a right place for derivative traders where they're really betting on the slope here rather than the absolute value of the in destination.

And I think. That it is a really important thing to know. I mean, Nate Silver, I think most folks know who Nate Silver is. Famously, you know, with his election predictions, he was showing trends and then when election outcomes went another way, he's like, it was still like a 20% probability of a different outcome.

So I think people are looking for security. But what we can see is that the long-term trend lines, whether it hits in 20 30, 20 35, 20 40. If you've got a kid who's a kindergartner, it is very likely to hit. And you know, you want them to be prepared. Some concrete elements. By 2035, there was some really great data around nurses and teachers expecting to see the highest growth in jobs driven by hands-on needs while janitors and construction workers see muted growth because their hands-on needs are counteracted by robotics or automation or decreased.

Real estate demand, and then software sales, finance and law expected to see sharp staff reductions. Those have historically been fields, which were path to the middle class. So I think, you know, if you're rooting for the teacher supply chain, you're like, this could be great for awesome teachers. If you're thinking about own paths of economic mobility, you've gotta think about.

Things much, much differently. As you've rolled this out, what have been the reactions that you've seen to the data? Are there certain groups that seem encouraged or discouraged, and what do you think is going to be an outcome of the ongoing nature of this work? 

[00:23:30] Kumar Garg: Yeah, the reaction was super interesting. I mean, part of the reaction has just been that, you know, we interact a lot with donors.

We interact a lot with decision makers. I think people are immediately trying to figure out how can they put it to use in their work. Which I think is fascinating. I think the other piece is. People have also flagged interesting data sources that they use to sort of get better sense. And so we're like, oh, should we keep a running list of, you know, what are all the indices that people look at to pull in?

Because those can all be sort of fed into the Meac platform and be made available to the super forecasters for icl. I think the other piece that I've just sort of seen is that, you know, I don't know if everyone was tracking this community of super forecasters as like a capability that we should be leveraging more.

And so it's also, you know, because I think sometimes people think about it just prediction markets. But prediction markets are, I would say, like a cousin to forecasting, which is prediction markets are individuals making bets that if the market gets liquid enough. And sentiment starts to move on. A very precise question, like, who's gonna be the next Fed chair That might give you some early signal about what's about to happen based on something that you may not know, but now a set of insiders do know.

I think forecasting is more of a reasoning platform. You know, it's like people who are just seem to be very good at reasoning, through uncertainty. And so it's a, it's a nice compliment. Part of the reason why we did this was we had not seen. This community being leveraged on these sort of labor questions.

I think it'll be a useful push. 

[00:25:04] Ben Kornell: I mean, it raises questions of what else should we use this community of super forecasters for? That's great. Folks should take a look, and we'll include this in the show notes, but also you paired the forecast. With literature from research. And so I think the idea of getting a 360 view, where it's like, what does the research say, what do the forecasters say?

What are the trends that we notice on a over time basis allows you to kind of triangulate into okay, directionally where things are headed. That kind of conceptual framework in a highly volatile time. It could be applicable to science and medicine and could be applicable Also, I think you have a, a sub study around Washington state like this could also at a different grain size could be really, really valuable to policymakers, investors, and institutional leaders.

[00:25:58] Kumar Garg: We get a lot of interest on the regional question, as you might imagine, like. Governors decision makers who care about a particular region, like what does this mean in our region? And so I think that's a useful area to push as well. And part of the reason we worked on testing some questions of Washington State was just to get a little bit more color on national versus regional.

[00:26:18] Ben Kornell: Okay. So I'm gonna wrap us up with a forecast of your own Kumar. So as you look forward and you think about the future of the philanthropic space. And how forecasting will play in it and how Ren Phil will play in it. You know? What does five years from now look like? Just given that you've scratched the surface on some bringing science and financial methodologies into social impact and philanthropy, what's your prediction on where this space goes over the next five to 10 years?

[00:26:49] Kumar Garg: It's a really good question. I think one forecast I just have is that we are experiencing a pretty strong. Partly just because of how AI has developed, but just sort of in general, the number of wealth holders. You know, folks with significant resources has continued to rapidly expand both in the US but also globally.

Two thirds of those folks are outside the us. I think those trend lines are gonna continue. So if you sort of think about as a numerator versus the denominator, the denominator of potential giving. Is rapidly expanding. I mean, you mentioned DAFs and that's like pre-committed capital, but even just on wealth alone, the numbers are gonna expand.

I think the next two years on the AI side, on the wealth generation with all the IPOs will be quite substantial. So I, I would say like we're gonna see huge increases on the denominator, I think on actual deployment. I think it's a big question. I mean, our bet is that even just to sustain the numerator, like giving rates are.

There was a Bridgespan Sunny from a few years ago that said giving rates were south of 2% for folks who are in that category. 

[00:28:00] Ben Kornell: And that one highlighted in particular, that tech people with tech backgrounds in Silicon Valley. Giving was depressed. 

[00:28:08] Kumar Garg: Yeah. And part of it is just a percentage game. Like for some folks who have done well, that 2% is a really large number of annual giving.

But I think like step one is like, we shouldn't lose ground. We should actually be making ground. And so that's our bet. Like not only should we be trying to sustain that numerator, we should be actually eating into the denominator in serially substantial ways so that I'm like less confident. I think we have early momentum.

We have, uh, partners and peers like coefficient giving that are very focused on trying to move money from the denominator and the numerator. We're trying to, you know, inspire others to sort of use this fund model to sort of deploy capital and make a run at it. So I feel pretty confident. The denominator, I think the numerator in our hands.

So we've got a lot of work to do. 

[00:28:54] Ben Kornell: Well with that, we are gonna wrap up today. I'm cheering for the numerator to go up, as is everyone on this pod. And I will say the work that you've done is not only inspiring for what Ren. Phil. is doing, but structurally like opening these new avenues for. Thinking about philanthropy alongside policy, alongside research, really catalyzing social impact and change.

So thanks so much for joining us today, Kumar. If people wanna find out more about Ren Phil's work, what's the best way for them to learn more? 

[00:29:27] Kumar Garg: Yeah, we have a website where we put all of our playbooks, all of our funds, and a lot of our insights. So that's renaissancephilanthropy.org. We launched a sci-fi substack recently.

You can check that out too. And then I'm on all the socials so you can find me there. 

[00:29:41] Ben Kornell: Awesome. Kumar Garg, President of Renaissance Philanthropy. Thanks so much for joining EdTech Insiders today. 

[00:29:48] Kumar Garg: Thanks for having me. 

[00:29:49] Alex Sarlin: Thanks for listening to this episode of EdTech Insiders. If you like the podcast, remember to rate it and share it with others in the EdTech community.

For those who want even more, EdTech Insider, subscribe to the Free EdTech Insiders Newsletter on substack.

Innovation in pre-K to grade learning is powered by exceptional people. For over 15 years. Ed tech companies of all sizes and stages have trusted higher education to find the talent that drives impact when specific skills and experiences are mission critical. Higher education is a partner that delivers offering permanent, fractional, and executive recruitment.

Higher education knows the go-to-market talent you need. Learn more@hireedu.com. That's HIRE edu.com.