3 formulae for venture philanthropy

🤓 3 formulae for venture philanthropy

TL; DR – This piece proposes 3 formulae for anyone allocating capital for social return: how much impact to expect, how likely you are to get it, and whether it would have happened anyway. Use the formulae to stretch and deepen your thinking, rather than replacing it.

Two things are true about the world right now:

There’s more money around than ever before (albeit unevenly distributed).
There’s a lot of things that need fixing.

We urgently need to direct all that capital effectively, to fix problems and make the world a better place. How do we do it?

One approach is funding new ideas — basically treating philanthropic capital like venture capital to seed novel, impactful solutions (sometimes called ‘venture philanthropy’ or ‘catalytic capital’. I’m biased towards this, as I’ve been doing it for almost a decade. But it’s hard work.

There’s deep technical complexity in introducing new ideas to new places, where uncertainty is high. This piece proposes three formulae to break that down. The point isn’t to magically use maths to make decisions. Rather, it’s to use maths to trigger deep and critical thinking. The formulae are a starting point, for anyone considering allocating capital for social return.

I originally put this thinking together as a slide deck for a friend (who leans quant). The original slides are below, and the text is adapted from my voiceover.

The danger of codifying ideas into a playbook is that it becomes a rigid checklist. Reality is messy, so it goes without saying that not all five steps above apply to every product or service looking to create impact at scale. And certainly, the order is fluid (although I’d always suggest starting with #1).

Playbooks can be reductionist. They make people follow rules instead of thinking for themselves. So, why bother building them?

Because of the importance of the ‘default’³.

In other words, the importance of a sector-wide, shared mental model for how, in the majority of cases, we get from zero to impact at scale.

The power of defaults is visible in how Silicon Valley has codified the science of scaling commercial startups. It starts with product-market fit (PMF). The startup shows traction through a range of widely known metrics: revenue run rate (RRR), average revenue per user (ARPU), lifetime value (LTV), the ratio of LTV to customer acquisition cost (CAC), and so on. Once you’ve demonstrated traction, you raise pre-seed, seed, and Series A, B, C and so on. The key is to bridge the valley of death, getting to sustainable revenue before your runway runs out. If you’re lucky to reach the other side of the valley, you might show hockey stick growth, and huge profitability.

There’s a default pathway to scale for commercial startups, with its own language (and acronyms), sketched out over the last three decades and exported worldwide. Founders, VCs, accelerators, startup employees, and everyone else in the ecosystem can use it as a rough guide, tracing proven steps as they grow. For Founders in particular, having a default gives you the means to benchmark yourself against others, to celebrate wins (raising a funding round, hitting a positive LTV:CAC ratio, etc), and to plan ahead with the map already sketched out.

Silicon Valley has built the default for scaling startups to massively profitable enterprises.

Now, let’s build the default for scaling new ideas to massive social change.

Those ideas might be from a startup. Or, they might come from a NGO, a government team, or a grassroots organisation. The playbook stays the same, whatever the venture.

The social return we’ve calculated above isn’t 100% certain. So, how certain is it?

We can estimate this by isolating each critical uncertainty, allocating it a percentage likelihood, and multiplying those together.

Given so much of our social return is dependent on catalysing breakthrough ventures, critical uncertainties will most likely relate to our ability to find, fund, and support enough (~10%+) of them.

This was certainly the case with the Assistive Tech Impact Fund. Looking back, our critical uncertainties were:

CU₁: finding viable business models. Scaling meant generating income (whether B2C, B2B or B2G) and profitability. This was notoriously difficult in the assistive tech sector, especially in African markets. We needed 5 viable models in our portfolio, with one that could take off and sustain national-level scale. Let’s estimate likelihood at 60%.
CU₂: finding products people love and use. Assuming viable business models exist, and we can find them, our social return remains contingent on consistent usage. This means a robust product, easy to use and maintain, that genuinely brings value and joy. We’d seen startups building great products — ultimately, some of them ended up as our portfolio companies (including Koalaa’s prosthetics, and Wazi’s glasses). Let’s estimate likelihood at 80%.
CU₃: startups can absorb relatively large cheques. We were asking small organisations to spend ~$200K responsibly, speedily and effectively. In the context of Africa’s assistive tech sector, this was a relatively large cheque. Let’s estimate likelihood at 70%.

Taking these three critical uncertainties and multiplying them, this sets our overall likelihood of social return at ~34%. And there’ll always be things we hadn’t considered, that would drag this percentage down further.

Important note: this formula only works if each critical uncertainty is fully independent. For example, if an assistive tech product is loved and used, a user will pay for it, making a viable business model more likely. The two are connected.

While we can try our best to keep the critical uncertainties as specific and independent as possible, it’s basically impossible in practice. So ‘unknown unknowns’ drag down the ~34%, while CUs’ lack of independence drags it up. Hopefully, the two cancel each other out 🙂

Would the social return we hope to generate would happen anyway?

After all, we’ll never be the only funder for our venture portfolio. Other funds exist, or will do in the near future, with similar goals. Our second formula asks us to understand two key aspects of the funding landscape:

p(others would fund our venture portfolio) = What’s the probability that our ventures would receive capital from another funder, for similar ends? Let’s cap the time horizon to 10 years, because no-one knows anything beyond that.
years gained by doing it now = How long before the progress we’re catalysing would happen anyway? This progress can happen in a few different ways: via government action (policy change, public sector spending, etc), market action (companies act on commercial opportunities), or cultural shifts. Again, let’s cap this at 10 years gained.

Again, let’s work through what it would have looked like with the Assistive Tech Impact Fund:

The p(others would fund our venture portfolio) is low — perhaps ~40%. While there were a couple of players in the space when we launched in 2020, it wasn’t) many. Funding for persons with disabilities is still overlooked, relative to other health domains like infectious disease — let alone catalytic capital for new ideas.
The years gained by doing it now is high — let’s go with the max of 10 years. Governments, markets or culture shifts would not, we believed, meaningfully scale assistive tech across the vast majority of Africa. Stretched health budgets, a lack of ‘payer’ in most cases, limited advocacy, and limited technological progress mean there’s little medium term potential for progress towards social return without catalytic capital.

The lower the result of the equation, the better. In our case, we’d score (40% / 10) = 0.04.

A high score (say >0.15), and we should consider the wisdom of the fund — your portfolio would be funded anyway, and the biggest sources of capital (governments and markets) and behaviour change are already primed to scale things without you.

A mid-level score is a bit more complicated. It means either:

p(others would fund our venture portfolio) is high. This could be a prompt to partner with others: pooling capital, splitting up target geography, and so on.
Years gained by doing it now are low. If the thesis space is urgent, it might still be worth jumping in to accelerate the timeline further. A fund to mitigate CO2 emissions might fall here.) Or directing your capital to something more catalytic.

Armed with these three formulae, we should be able to say:

We estimate this fund has a [x]% chance of achieving [y] social return.
And based on the landscape, we should [describe course of action]

And more importantly, we should have some critical thinking and conversations under our belt. Capital is such a powerful lever for social return — we owe it to ourselves to use it in the smartest possible way.

🤔 Got thoughts? Don’t keep them to yourself. Email me on asad@asadrahman.io. Let’s figure this out together.

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Banner depicts Conarky, the capital of Guinea. From Wikimedia Commons.