Kumar Garg is the President at Renaissance Philanthropy, a US-based non-profit that, inside its first two years, catalysed greater than $500 million in philanthropic funding globally for science, expertise, and innovation.
The organisation has launched 22 time-bound, thesis-driven philanthropic funds and programmes addressing world challenges. These embody superior analysis to sort out local weather emergencies, supporting breakthrough concepts for pupil success, responsibly and quickly scaling geologic hydrogen and offering seed funding to researchers and technologists for bettering social service supply.
Prior to becoming a member of Renaissance Philanthropy, Garg labored with Schmidt Futures, the place he helped design and launch moonshot initiatives in training. Before that, he helped set budget and coverage priorities for the Obama Administration as a part of the White House Office of Science and Technology Policy.
Kumar holds a BA from Dartmouth College and a legislation diploma from Yale Law School.
In an interview with indianexpress.com, Garg speaks concerning the objectives and the construction of Renaissance Philanthropy, the moonshot concepts the organisation funds, the hard-to-solve issues in tech, and the influence they’ve created. Edited excerpts:
Venkatesh Kannaiah: Tell us about your journey to Renaissance Philanthropy.
Kumar Garg: I studied political science and laptop science in school and was fascinated with authorities, so after graduate faculty, I ended up serving within the Obama administration. I labored for President Barack Obama’s science advisor and obtained publicity to how science and expertise coverage is developed throughout a variety of areas — house, commercialisation, superior manufacturing and math and science training.
Story continues beneath this advert
After that, I went to work for Eric Schmidt of Google and helped construct the Science and Tech Foundation, Schmidt Futures. It centered on how philanthropic capital might advance totally different fields of analysis and apply them to the general public good.
Renaissance Philanthropy was basically a spinout. The core group got here from working immediately with Eric Schmidt.
Venkatesh Kannaiah: What was the concept behind Renaissance Philanthropy?
Kumar Garg: The thought got here from recognising that the massive challenges like local weather, AI, training, and financial mobility are issues we have to deal with right this moment and not later.
So the query turned: might Renaissance construct high-quality science and expertise programmes — accelerating center faculty math with AI, creating new vaccine platforms, figuring out new vitality sources like geologic hydrogen — and construction them extra like funding funds, however for philanthropy?
Story continues beneath this advert
The mannequin was to construct technical programmes and philanthropic funds round particular sectors, and then go to donors and say: Rather than constructing all of this internally, you’ll be able to take part nearly like a Limited Partner in a enterprise fund.
If we would like extra capital deployed towards onerous issues, we want extra automobiles that make it simpler for folks to take part. That was the fundamental thought.
In two years, we’ve helped transfer about half a billion {dollars} — roughly $250 million immediately and one other $250 million by way of advisory assist.
Venkatesh Kannaiah: How do you design your programmes?
Kumar Garg: Our start line is that philanthropy is just one small a part of a a lot bigger system. So the query for us is: what sort of intervention could make a considerable influence inside three to 5 years, in a approach that the broader area can finally maintain and construct upon?
Story continues beneath this advert
A variety of the way in which we design our programmes comes right down to figuring out actually onerous issues in science or training the place centered R&D and technological innovation might make a significant distinction.
Venkatesh Kannaiah: Tell us about a few of your programmes that are creating an influence.
Kumar Garg: For instance, in center faculty math, we checked out a 2012 J-PAL research that confirmed intensive, shut tutoring assist might double the speed of studying.
The problem was price. The research discovered it price round $4,000 per pupil. That’s costly even within the United States. We are whether or not we are able to carry it right down to $500.
Story continues beneath this advert
It is a five-year programme involving a number of groups, all working in the direction of the objective. They’re utilizing AI, combining it with established tutoring science, and integrating totally different instruments and strategies right into a coherent system.
It could be very troublesome to fund the R&D wanted to make this type of work doable. These initiatives require making use of rising applied sciences, working lots of of pilot research, and testing totally different implementation fashions.
We even have an initiative centered on early studying. Here, AI is used for screening and evaluation.
One of the key challenges is that educators don’t know a toddler’s precise studying stage, or whether or not the kid might have undiagnosed studying difficulties similar to dyslexia or speech-related points. Those screenings are presently costly and troublesome to manage at scale, which suggests many kids by no means get assessed correctly. Without that info, it’s onerous to know what intervention is required.
Story continues beneath this advert
AI techniques are already superb at automated speech recognition for adults, however far fewer folks have centered on constructing speech recognition techniques that work nicely for kids.
Another space is tooling for mathematical analysis utilizing AI. We awarded over $18 million to researchers globally to construct instruments that make it simpler for mathematicians to make use of AI of their work.
We are already seeing a few of the instruments unfold shortly inside the analysis group. For instance, Lean is a programming language which permits mathematical proofs to be written in a computationally verifiable type. Some of the early grants we supported are already influencing how mathematicians collaborate and the sorts of instruments they use of their day-to-day work.
There is the Public Benefit Innovation Fund. The thought is to enhance one of many main capabilities of presidency, like delivering advantages and providers to residents. In the US, estimates counsel that almost a trillion {dollars} in advantages go undelivered due to administrative inefficiencies and system complexity.
Story continues beneath this advert
So we requested whether or not rising applied sciences like AI might enhance how these techniques function. A easy instance is name centres. Many authorities businesses battle to reply all incoming calls, even for fundamental questions like confirming whether or not an utility has been obtained or whether or not somebody qualifies for a programme.
Some of the grants we supported have led to deployments in US states, the place new instruments now assist with eligibility checks, automate routine assist duties, and monitor coverage or code adjustments extra successfully.
We’re significantly fascinated with serving to governments undertake AI in a extra experimental and evidence-driven approach.
Venkatesh Kannaiah: Tell us about your accelerator programme.
Kumar Garg: We run a programme known as the Big If True Science Accelerator. The thought behind it’s that formidable researchers typically don’t obtain a lot teaching on creating a very transformative thought. They’re normally centered on working their labs and making use of for grants.
Story continues beneath this advert
So in our accelerator, we provide 15 weeks of teaching and introduce them to governments, donors, and different potential supporters.
Our broader objective is to extend the ambition of each donors and scientists. We’re presently on our third cohort, with round 45 scientists having gone by way of the programme thus far.
Venkatesh Kannaiah: Tell us about moonshot concepts that you’re funding or seeking to fund.
Kumar Garg: One, which I already talked about, is literacy work, whether or not we are able to reduce in half the variety of kids battling studying by third grade by way of higher identification of early studying difficulties utilizing AI.
Another space is the position of hidden well being burdens in studying outcomes. One instance is air high quality in colleges. Research reveals that cleaner air in lecture rooms has a significant influence on each studying and well being outcomes. Even comparatively easy interventions, like higher air purification techniques, can have very excessive returns.
So we’ve been exploring a programme on constructing cheaper, extra deployable air purification applied sciences and ensuring they’re truly utilized in colleges. We’re exploring that each within the US and globally.
Another space we’re fascinated with is lead air pollution. Lead publicity can have main impacts on IQ and long-term well being outcomes. One thought we’ve been is whether or not we are able to construct a significantly better blood check for detecting lead publicity. The check broadly used right this moment was developed roughly 40 years in the past and just isn’t exact.
We’re the place AI can remodel scientific analysis itself. We have already got a programme in arithmetic, however we’re fascinated with different domains as nicely.
One space I discover very compelling is monsoon prediction. The Indian authorities has spoken about this problem, however I believe there’s nonetheless room for a way more centered push round superior prediction fashions.
We’ve spoken with researchers who’ve modelled the social advantages of improved monsoon prediction. We’ve additionally begun conversations with donors and governments about whether or not there might be assist for a extra concentrated effort on this space. It remains to be at an exploratory stage, nevertheless it’s one thing I’m personally very fascinated with due to the dimensions of potential influence.
We’ve been doing work round geologic hydrogen — the concept that as an alternative of producing hydrogen by way of industrial processes, you can extract naturally occurring hydrogen immediately from underground sources. If it proves viable, it might change into an essential software for decarbonising sectors which can be in any other case very troublesome to transition. The dialog now could be about constructing higher subsurface maps, figuring out pilot alternatives, and understanding the place the useful resource potential might exist.
We have additionally been exploring the concept of potassium-enriched salt. There’s already sturdy randomised managed trial proof suggesting {that a} vital share of heart problems could also be linked to potassium deficiency. By barely rising the potassium content material in salt — with out altering the style — you might be able to meaningfully enhance population-level well being outcomes.
That might be particularly essential in nations with excessive charges of hypertension, together with each India and the United States.
One of our programmes seems to be at whether or not it’s doable to construct a brand new era of space-based telescopes that generate vastly extra information at a fraction of the price of conventional techniques.
Venkatesh Kannaiah: Tell us about science and tech concepts which you assume are very onerous to crack.
Kumar Garg: I believe biology is extraordinarily onerous. People typically say, “If we can just figure out this one thing, then AI will solve the rest,” or that after a selected breakthrough occurs, the whole lot else turns into simple. But what we preserve discovering is that the deeper you go, the extra advanced the system seems to be.
Take most cancers, for instance. Globally, we’ve made huge progress, and that progress is constant. But the extra we study, the extra we realise it’s not one illness — it’s 1000’s of various subtypes and organic pathways. So at the same time as advances in biology speed up, it stays a deeply advanced, depraved downside that can require many sensible folks engaged on it from a number of angles.
Another problem is ensuring we take into consideration science and expertise issues when it comes to bottlenecks. People typically assume that fixing one instant situation will unlock the whole lot else. But in apply, innovation techniques are normally constrained in a number of methods directly.
For instance, we’ve been exploring a programme round scientific trials — particularly, how you can speed up their tempo. Most folks don’t instantly consider that as a science and expertise problem. They assume science means inventing the following drug or remedy. But if the scientific trial system itself is gradual or inefficient, then all of that innovation will get bottlenecked as a result of new therapies can’t attain the market.
We have a fellow on our group primarily based in India who has been engaged on how India might modernise its Phase 1 scientific trial system. Right now, the method has change into more and more gradual, whereas nations like China are shifting a lot quicker. As a consequence, many promising concepts and corporations merely go elsewhere as a result of they’ll’t get trials began effectively.
That’s one of many key classes we attempt to emphasise: when fascinated with troublesome science and expertise issues, it’s important to think twice about bottlenecks.
Sometimes the bottleneck is regulation. Sometimes it’s a scarcity of expertise. Sometimes it’s that two totally different scientific fields aren’t speaking successfully with one another. Sometimes it’s funding. But folks typically mistake probably the most seen or instant problem for the one problem.
Venkatesh Kannaiah: Do you’re employed with startups? And if that’s the case, how do you have interaction with them?
Kumar Garg: So the way in which our mannequin works is that we elevate cash philanthropically. The donors who assist us are writing cheques with out anticipating monetary returns. The capital comes into the organisation and is then allotted to particular funds or programmes.
Once cash enters a selected programme, the fund chief has broad discretion over the way it must be deployed. That might imply issuing a grant, making a present, funding a contract, and even utilizing instruments like loans or mission-related investments.
Venkatesh Kannaiah: How do you have interaction with governments and native innovation ecosystems?
Kumar Garg: We have a lot of authorities partnerships, most of them with nationwide governments. For instance, we associate with nationwide innovation businesses like ARIA within the UK, SPRIND in Germany, and we not too long ago signed a partnership settlement with the Cabinet Office in Japan.
A giant purpose governments work with us is that they wish to make their R&D ecosystems extra formidable. They wish to determine probably the most formidable researchers of their techniques and assist them do their finest work. A variety of our programmes are designed particularly to determine these researchers and coach them, which governments discover beneficial.
Venkatesh Kannaiah: What are your views on the Indian innovation ecosystem?
Kumar Garg: I believe the Indian innovation ecosystem has many strengths. It has a deep engineering base, large manufacturing capability, and main capabilities in areas like prescribed drugs.
India can be deeply related to the broader Western science and expertise ecosystem, partly due to English and partly due to the Indian diaspora in locations just like the United States. Between the IIT system and the broader technical ecosystem, there’s already an excessive amount of scientific and engineering depth.
I believe the massive problem now could be how you can assume extra systematically about formidable R&D programmes in India. Often, you’ll see glorious particular person researchers doing sturdy work, or establishments with attention-grabbing partnerships and pockets of innovation. But for those who ask questions like, “What is India’s equivalent of the UK Biobank?” — which means a large-scale, deeply structured, high-quality nationwide analysis dataset that many researchers can construct on — it’s not clear what these giant shared moonshot infrastructures seem like.
The query is: what’s the social infrastructure for designing and launching these moonshots? Who is doing the early-stage scoping work, figuring out the expertise, convening workshops, creating the primary pilot research, and constructing the preliminary momentum earlier than governments step in at scale?
I believe constructing extra of that tradition round formidable experimentation can be extraordinarily beneficial in India as nicely.