What I’ve Learned about VC from Four Years at UNDP's Accelerator Labs

So, this is a bit of a departure from my regular What I’ve Learned series. The goal of those pieces is to walk myself toward an informed point of view on venture capital opportunities in digital commerce, fintech, and logistics in Africa. This time I’m sharing some of what I’ve learned during my time with UNDP’s Accelerator Lab network, because it speaks to what I’m exploring next (hopefully).

In a fit of self-righteous outrage, I wrote a lengthy email to someone more knowledgeable about VC to share my initial thoughts and ask questions. We went back and forth for a few rounds, but he ultimately concluded that I didn’t understand VC. In retrospect, he was right. But so was I. VC is ideally about investing in high-growth businesses that are derisked at each stage while increasing in value. Sometimes, this pursuit results in category-defining, wildly valuable companies while making a lot of money for people. Other times, investors earn high returns selling their stakes in businesses that turn out not to be particularly enduring or valuable. And still other times, everyone loses money on companies that aren’t enduring or valuable.

Fortunately, I got my first taste of VC (well, maybe VC-adjacency), a couple of years later while supporting XL Africa, an acceleration program at the World Bank to support early-stage tech companies. But soon after, I started to explore design thinking and then systems thinking. As part of that process, I took Systems Practice, a free introductory course on systems thinking offered by the Acumen Academy. That’s where I learned (or perhaps relearned) a very basic insight—systems problems are complex, while other difficult problems are just complicated. The course described these as cloud problems and clock problems, respectively. Here’s how I understood the difference:

A complex problem doesn’t follow a linear path from challenge to solution because there are ever-changing dynamics that affect where and how your intervention lands and what happens as a result. It’s also difficult to attribute results to any single action. Complicated problems, by contrast, can be hard to solve—and many great startups do solve them. But once you find the solution, there’s a relatively straight line to scaling that solution and maybe even being compensated for it.

It was that early sense of cognitive dissonance, or things not quite adding up, which sparked my sense of curiosity. Everything since has been a slow but steady attempt to learn enough to properly interrogate that intuition. After the World Bank stint, I spent three years doing independent research about innovation and enabling infrastructure for entrepreneurship. Chasing Outliers: Why Context Matters for Early-stage Investing in Africa was the last report I produced before joining UNDP, and compared fundamental assumptions driving Silicon Valley–style VC and the market characteristics that enable them, with what might be considered fundamental market characteristics in Africa. Let’s just say I learned A LOT speaking to 100+ founders, investors and LPs. But it left me with more questions. That’s when I launched
The Trajectory Africa podcast as an experimental learning platform to keep looking for answers. And my writing is a way to process and share what I’ve heard and learned from the pod.

Long story short, I’ve been in search of what lies within and beyond the purview of VC, by acquiring minimum effective doses of knowledge about traditional VC, how it’s evolving in Africa, and of how systems thinking might be applied to investing. The goal is basically to properly explore what I perceive to be mismatches between the types of problems on the continent that need solving, and the types of capital being deployed to solve them.

At this point, you’re probably wondering what any of this has to do with UNDP. Well, the basic premise underlying the Accelerator Labs is that confronting complex social problems requires rapid, iterative learning driven by people close enough to the problems to gain critical insights about the systems they inhabit. Through exploration, experimentation, and solutions mapping, learning can inform where and how to intervene in systems.

So, what I’ve learned from my time with the Labs is the practice of distilling tacit knowledge. This is the practical insight held by people “in the arena” working with systems, communities, and their problems. But some of that systems knowledge is tacit, or embodied in the practice of doing the work. Bas Leurs, the network’s lead learning designer, likes to quote Dave Snowden as a way to explain this: We always know more than we can say, and we will always say more than we can write down. In other words, a lot of knowledge remains submerged, but it can be surfaced if you ask the right questions in the right environment.

Helping to excavate this knowledge has been a large part of my job as the network’s Learning & Community Manager. Essentially, I’ve been supporting the practice of codifying and sharing knowledge about topics that multiple Labs were exploring in different contexts, at their own discretion. We’ve never told them what to work on, but we’ve tried different approaches for translating tacit knowledge into actionable insight that nudges systems toward the resolution of seemingly intractable problems. What does this look like? Read on…

In 2022, the Accelerator Lab Network had a midterm evaluation, and the evaluator commented on the utility of the “R&D” emerging from the network. Labs were setting their own agendas, tackling the problems they found most relevant, sharing what they were learning, and generating patterns of collective insight. Understanding where there was a critical mass of activity—say, around work on the circular economy or food systems—allowed us to start codifying that learning. That recognition led to the definition and testing of what came to be called open R&D.

We prototyped this function across three topics: circular economy, digital financial inclusion, and food systems, working through the following three steps:

1. Recognize emerging patterns, or where a critical mass of Labs is working on similar issues. By combing through the artefacts that Labs produce, such as blogs, learning plans and reports, we’re able to get a sense for where the action and energy are.
   

2. Codify the learning, to describe what Labs are doing, how they’re doing it, and what patterns are emerging. Initially, this was captured in quick and dirty PowerPoint decks, but as the practice evolved, it was captured in collections on our open innovation platform and in toolkits like this one about digital financial inclusion.
   

3. Curate collective exploration, by bringing together Labbers, field experts, and potential funders to engage with critical questions that might reveal system leverage points. The prototype of this process started with desk research to map existing knowledge, continued with expert consultations to refine frontier-finding questions, and culminated in a curated three-day workshop (a “Rave,” in Accelerator Lab parlance). The goal isn’t to produce answers, but to co-create an R&D agenda: a theory of change or hypothesis for what could make a difference, based on what we’d collectively learned. The theory of change is accompanied by learning questions articulating knowledge gaps that signal where to intervene, along with research activities designed to answer questions and fill gaps.

My key takeaway from co-leading the Rave on Digital Financial Inclusion—the first in a series of three—is that curated conversation is a powerful way to surface tacit knowledge that helps us understand systems more deeply, and eventually pinpoint where to act.

Assuming the premise holds that many of the infrastructure-building, vertically-integrating startups in Africa are tackling complex rather than complicated problems (especially in sectors like agriculture, education, energy, and healthcare), then we may need ways to cultivate and surface tacit knowledge from those immersed in these systems—the users, the solution creators, and the investors.

VC largely rewards monopolistic solutions—one winner scales, and everyone else dies. But many social challenges might benefit from portfolios of solutions that are mutually reinforcing, like how cheap and accessible phones and data, regulatory sandboxes, consumer education, and instant payment systems, complement fintech innovation. Not all of these investments are appropriate for VC capital. But together, they maximize opportunities for financial inclusion and fintech-powered economic productivity. In short, there’s a case to be made for making this kind of collective intelligence visible and actionable. That, in essence, is what I’m taking with me from UNDP’s Accelerator Labs—the belief that surfacing tacit knowledge can be a powerful tool for confronting complex social problems, and may offer insight into how to productively fund solutions to these sorts of problems in Africa.

Of course, I still have a lot to learn: about VC, how it works in Africa generally, and where the opportunities like in specific sectors and geographies. I’m also beginning to study the emerging field of systemic investing to understand if/to what extent investing from a systems perspective could work on the continent, so people can get cheap and ubiquitous access to financial services, education, energy, food, and healthcare.

So, if you’re thinking about or experimenting with these ideas, please reach out. I’m actively looking for people in the arena, and I’d love to hear from you.