Investor Propositions

“We are at an evolutionary point where now VCs are much happier to share deals. If I see a good deal that isn’t the right fit for us, I'll share it with other VCs. There is a lot more collaboration happening now. And hopefully people can have longer term views rather than just short term gains. ‘A rising tide lifts all ships’, as they say.” - interviewee

The correction period has been the investor ecosystem’s most significant stress test. The ventures and investors that navigated it best appear to be those that embodied the principles described here: syndication, sharper curation, better information, operational support, cleaner deal structures, stronger leadership assessment and deliberate gender-lens allocation.

Syndicates - structured vehicles enabling multiple investors to co-invest through special purpose entities - can reduce concentration risk and improve access to diversified deal flow. The structural foundation for understanding why syndication can be more than a risk-distribution mechanism sits in modern empirical VC research. Hochberg, Ljungqvist and Lu's Journal of Finance paper "Whom You Know Matters: Venture Capital Networks and Investment Performance" - drawing on comprehensive US VC syndication network data - established the foundational empirical pattern: a VC firm's position in the syndication network materially predicts both deal-level and firm-level investment performance, with network-central firms achieving higher exit rates and superior fund returns. The mechanism is informational and operational: syndication networks can aggregate due-diligence intelligence, distribute monitoring obligations and support coordination on follow-on investment in ways that single-investor positions struggle to replicate.

The implication for the African ecosystem is structural. In thin markets, the Hochberg-Ljungqvist-Lu network effect may matter even more: the smaller the universe of investable opportunities, the more consequential network position becomes for which ventures access the syndication intelligence that distinguishes scalable opportunities from premature ones. Syndication is not just risk-sharing; in thin markets, it can become a coordination mechanism through which investors generate collective intelligence that single-investor deployment cannot easily produce.

The Future Africa Collective provides a useful working model: a structured syndicate with defined membership, minimum commitments, and coordinated deal participation. It lowers the barrier to entry for early-stage investing while maintaining a degree of discipline in selection and execution.

The correction period reinforced the case for syndication. Investors concentrated in single-company positions were more exposed when funding conditions tightened. Diversified syndicate structures may offer greater portfolio resilience, not because individual deals necessarily perform better, but because risk is distributed and follow-on coordination can be institutionalised.

But generic syndication is only a partial solution. The stronger proposition is vertical-specific syndication. Sector-focused syndicates - organised around fintech, climate, health or logistics - can enable deeper domain expertise, more informed diligence and stronger post-investment support.. They also create tighter signalling loops between investors and founders, reducing information asymmetry within specific market segments.

This becomes particularly relevant at the Series B stage, where capital requirements increase but risk remains high. Public–private syndication models, in which DFIs and private investors co-invest within structured vehicles, may offer one credible response to this gap. By blending risk tolerance and return expectations, they can help unlock capital that would not flow through purely commercial channels. The substantive treatment of how DFI deployment behaviour has converged toward commercial benchmarks - and the political-economy reasons reform is structurally resisted - sits in Institutional Actors and The Political Economy of Capital Allocation.

The Ci-Gaba Fund of Funds model - substantive treatment in Institutional Actors - illustrates the principle at the fund level: rather than relying on fragmented LP relationships, it creates a shared institutional vehicle that aggregates capital and distributes risk. The same logic applies at the venture level. Structured, sector-aligned syndicates can pool capital, concentrate expertise and provide a clearer pathway for ventures progressing toward later-stage funding.

“Curated deals for VCs would be highly valuable: the ability for tailor made and curated deals to be sent to us as VCs on a regular basis. That is something that I would imagine a lot of VCs would pay for. (Although we've seen this movie several times, and it hasn't had a good ending.) There needs to be an angel list of some sorts for Africa, for lack of a better term.” - interviewee

Since 2022, the data layer has shifted meaningfully, though unevenly. The substantive treatment of why data architecture in the African scaling ecosystem requires institutional design rather than goodwill - anchored in Hess-Ostrom on knowledge as a commons and Benkler on commons-based knowledge production - sits inData, Insights and Knowledge. The implication for investor information asymmetry is that the EADC programme has helped establish national-level ecosystem data infrastructure in Kenya, Ethiopia and Rwanda, with structured platforms designed to improve visibility on firm-level activity, capital flows and ecosystem dynamics. Crucially, these are not intended to be standalone data products: they are designed to connect with government systems and inform policy, strategy and ecosystem management — marking an early transition from fragmented visibility toward state-backed market intelligence.

Alongside this, a set of research and intelligence providers - Dealroom, AVCA, Disrupt Africa, Partech Africa, Briter Bridges, and Africa: The Big Deal - have improved the consistency and frequency of ecosystem reporting. These play a role in market signalling, but remain dependent on disclosed transactions and partial datasets.

The core constraint persists. Significant portions of early-stage capital remain undisclosed, coverage is concentrated in a small number of markets, and incentives to share firm-level data remain weak. While AI-assisted aggregation reduces technical barriers, the limiting factor is institutional: data governance frameworks, reporting norms, and aligned incentives for disclosure are still underdeveloped.

The founders who appeared to navigate the correction most effectively shared a specific set of capabilities: the ability to make hard resource-allocation decisions under uncertainty; the willingness to restructure teams when required; the capacity to maintain investor confidence while executing significant pivots; and the resilience to lead through sustained pressure.

These capabilities are not consistently captured by conventional due diligence. Personality tests and pattern-matching on founder backgrounds remain imperfect proxies for how leaders behave under stress.. The substantive treatment of upper-echelons theory and the empirical evidence on team-quality determinants of venture performance - anchored in Hambrick and Mason on upper-echelons theory, Beckman, Burton and O'Reilly on founding-team composition, and Wasserman's Founder's Dilemmas on founder-CEO transition dynamics - sits inThe Scaling Decision Log Decision 1.

Frameworks such as those developed by ScaleupNation point to a more structured and possibly more robust approach. Their scale-up audits assess leadership across dimensions such as managing complexity, training thinking rather than executing playbooks, balancing hard and soft skills, and maintaining the self-awareness required to delegate as organisations grow.. The substantive deployment of these dimensions in the leadership-team transition argument sits in Leadership and Human Capital.

The implication is clear. Founder assessment needs to shift from static indicators toward dynamic capability evaluation: how leaders make decisions, adapt and reconfigure organisations over time. AI-assisted due diligence tools may offer a meaningful step forward. By analysing communication patterns, decision histories and operational data, they may surface behavioural signals that traditional interviews miss. But these tools are not neutral. Without calibration to African operating contexts - where data is partial, teams are distributed, and informal systems play a larger role - they risk misclassification.

The challenge is not information scarcity alone, but interpretation. Better tools can improve signal quality, but only if grounded in the realities of how firms in these ecosystems actually operate.