The Missing Middle in Emerging-Market AI: Capital Flows Skip the Application Layer

Executive Summary

Artificial intelligence has become a defining technology of this era, and global capital is responding accordingly. But the distribution of AI investment is highly uneven, especially in emerging markets. A new analysis from Accendo Signals reveals that 94% of all committed AI investment in Africa, Asia, Latin America, and the Middle East goes into data centers and GPU clusters, while the applications that make AI useful for businesses, farmers, and hospitals receive just 2%. This structural imbalance—a "missing middle" gap—threatens to leave emerging economies with expensive infrastructure but little to show for it in terms of economic transformation. This article explores the findings, the commercial implications, and the strategic measures that can close the gap.

Introduction

The global race to build AI capacity is reshaping economies, but the benefits are not accruing equally across regions. In advanced economies, venture capital and corporate R&D flow into a broad mix of AI models, applications, and infrastructure. In emerging markets, however, a closer look at capital flows reveals a startling concentration at the physical layer—data centers and graphics processing units (GPUs)—with very little investment reaching the software, localized use cases, and services that could make AI a practical tool for development.

A new report by Accendo Signals, a research group that uses a swarm of AI agents and a small team to trace funding flows, tracked 159 committed AI transactions across Africa, Asia, Latin America, and the Middle East between January 2023 and August 2026. The disclosed total value: US$42.77 billion. The finding: 94% of that money funds the infrastructure AI runs on, while only 2% goes to the applications, platforms, and services that generate value for end users. Philanthropic grants account for a mere 0.17%, largely directed at local-language model development and small-scale experimentation.

Business Context

The "missing middle" is a familiar problem in development finance. Grants, seed capital, and early-stage venture capital help launch ideas, but a massive chasm often prevents those ideas from achieving scale. In the AI sector, this gap is particularly acute because the infrastructure layer absorbs nearly all available capital, crowding out the very ecosystem that would make that infrastructure productive.

Accendo Signals' analysis shows that most AI funding in emerging markets is concentrated in high-visibility, capital-intensive projects such as data centers and GPU clusters. Governments and global investors are eager to back these projects because they are tangible, strategically significant, and often tied to national digital ambitions. Yet the services that run on these systems—such as local-language AI models, agricultural advisory tools, healthcare diagnostics, and SME-focused business applications—receive a fraction of the attention and capital.

The result is an ecosystem that resembles a new kind of dependency: emerging markets are building the hardware but relying on imported AI software and expertise to make it work. In the long run, this might undermine the economic benefits of the infrastructure itself.

Main Analysis

The Accendo Signals data highlights three distinct funding gaps. First, there is a growth-capital gap for AI ventures. Most seed and early-stage startups in emerging markets can access a limited pool of angel investors and early-stage VCs. But when they need to expand, develop enterprise sales, or build localized products, the financing options thin out. The report proposes a growth-capital bridge specifically targeting tickets of roughly US$10 to 30 million. Such a facility would sit between the seed market and the deeper pools of capital that exist for later-stage scale-ups.

Second, shared data assets are underfunded. AI systems require high-quality, localized datasets—language corpora, evaluation benchmarks, and domain-specific data. These resources benefit hundreds or thousands of applications simultaneously, but no single company has the incentive to pay for their creation. Treating these shared digital assets as public infrastructure, akin to roads or electricity grids, could unlock immense value. Governments, philanthropies, and development finance institutions could fund the creation of open datasets and evaluation standards for emerging markets, lowering the barrier for local AI developers and making global AI models more inclusive.

Third, the current model finances supply, not demand. Governments in emerging markets often negotiate billion-dollar compute agreements with hyperscale providers, but they rarely attach conditions that support local adoption. The report suggests that these same governments should negotiate support for domestic developer ecosystems, enterprise adoption, public-sector workloads, and local AI companies. This would ensure that compute capacity is used productively and that the economic benefits of AI stay within the country.

Commercial Impact

The commercial implications of this funding imbalance are significant. For local businesses, the scarcity of application-layer funding means few AI tools are tailored to their needs. A manufacturer in Vietnam or a smallholder farmer in Nigeria may have access to global AI platforms, but these are often ill-suited to local languages, regulations, or infrastructure. This limits productivity gains and entrenches reliance on imported technology.

For global corporations and investors, the imbalance creates both risk and opportunity. The risk is that infrastructure assets underperform because the applications they support do not materialize at scale. Data centers and GPU clusters are long-lived investments that generate returns only if they are utilized. If the local ecosystem remains underdeveloped, these assets may become stranded. Conversely, the opportunity is for impact investors, development finance institutions, and corporate strategists to enter the "missing middle" and build the application layer while the market is still nascent. Being an early mover in a high-growth, underserved segment can yield outsized returns and strategic influence.

Strategic Insights

For investors, the report recommends a deliberate shift from funding hardware to funding the ecosystem around it. Impact investors, in particular, have a critical role to play in building the broader understanding and capacity for responsible and relevant AI. A growth-capital bridge would address the funding chasm, but it would also require technical assistance and risk-sharing mechanisms. Investees in emerging markets often struggle with regulatory complexity, limited talent pools, and fragmented customer bases, so investors must be prepared to offer more than just capital.

The concept of data as infrastructure deserves serious attention. Local-language datasets, evaluation resources, and anonymized public data are public goods. A coordinated investment in these resources would reduce duplication, accelerate innovation, and ensure AI models reflect the linguistic and cultural diversity of the markets they serve. Without such investments, AI in emerging markets will remain dependent on models trained elsewhere, perpetuating linguistic and cognitive bias.

Financing demand is a novel approach. Governments and public institutions are often the largest domestic buyers of technology. By leveraging their procurement power, they can influence the AI market. For example, a government negotiating a national compute project could require a percentage of the contract to be allocated to local AI companies, funding pilot projects for public hospitals, schools, or agricultural extension services. This would create a market for local AI applications and demonstrate their value to private-sector buyers.

Future Outlook

Over the next three to ten years, the way in which the missing middle is addressed will determine whether emerging markets become active participants in the global AI economy or passive consumers of technology developed elsewhere. The infrastructure build-out is likely to continue, driven by both private investment and geopolitical considerations. But the more important trend will be the emergence of hybrid financing vehicles, data cooperatives, and demand-side programs designed to close the gap.

We can expect to see more sophisticated public-private partnerships in AI, with governments recognizing that compute capacity alone does not translate into economic development. Impact investors will likely play a larger convening role, aligning infrastructure investors, corporate users, and local startups around a shared agenda. The growth-capital bridge could become a standard instrument in development finance, similar to the small-and-medium-enterprise (SME) lending programs that have evolved over the past two decades.

The long-term economic implication is that AI's value in emerging markets will be measured not by the number of chips installed, but by the gains in productivity, health, education, and financial inclusion. Building the missing middle is therefore not just an investment opportunity; it is a precondition for sustainable and inclusive growth in the digital era.

Conclusion

The Accendo Signals analysis offers a data-driven reminder that capital flows are not neutral. They encode strategic priorities and shape economic futures. In emerging markets, the current priority is unmistakable: infrastructure first, everything else a distant second. But the report also reveals a viable path forward. By building a growth-capital bridge, treating data as shared infrastructure, and financing demand rather than supply, investors and policymakers can correct the imbalance. The missing middle may be a gap today, but it is also an opportunity for those with the foresight to bridge it.

Key Takeaways

• 94% of US$42.77 billion in committed AI transactions in emerging markets (Jan 2023–Aug 2026) funded data centers and GPU clusters; only 2% went to applications and use cases.
• Philanthropic grants for AI in emerging markets represented just 0.17% of total funding, focusing on language models and experimentation.
• A "missing middle" gap exists between seed/early-stage funding and the deeper capital pools required for scaling AI ventures.
• Three interventions can address the gap: growth-capital bridges (US$10–30 million tickets), shared data assets as infrastructure, and financing demand-side AI adoption.
• Closing the gap is critical for ensuring AI infrastructure generates economic value and for enabling emerging markets to participate fully in the global AI economy.

SEO Keywords

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Sources

• Accendo Signals research as shared by Prateek Shrivastava on LinkedIn: Original LinkedIn post