Artificial Intelligence: A Catalyst for Upgrading or a Barrier to Entry for Africa in Global Value Chains?

Abstract: This study investigates whether Artificial Intelligence (AI) preparedness acts as a catalyst for structural upgrading or as a barrier to entry for African economies within Global Value Chains (GVCs). Combining, for the first time, the granular GVC positioning indicators (upstreamness) from Mancini et al. (2023) with the IMF's AI Preparedness Index (AIPI), we construct a novel dataset covering 11 African countries from 2000 to 2022. We address cross-sectional dependencea critical issue in a sample of interconnected economies-by employing Common Correlated Effects (CCE) and Interactive Fixed Effects (IFE) estimators. Our results reveal a nuanced reality. While standard models suggest a positive association between AI and upstreamness, our preferred IFE specification shows that, in isolation, AI preparedness is associated with a significant decrease in upstreamness (β =-0.042, p < 0.001), suggesting a risk of "premature automation" that confines countries to downstream segments. However, this effect is conditional on absorptive capacity: the interaction between AI and human capital reverses this trend, indicating that AI acts as a complement to, not a substitute for skills. Furthermore, Foreign Direct Investment (FDI) positively contributes to GVC upgrading only once common shocks are controlled for, highlighting the importance of knowledge transfer over mere capital inflows. Our findings imply that without strategic investments in "AI-ready" human capital and coordinated continental policies (e.g., within the AfCFTA), AI risks exacerbating technological bifurcation rather than fostering leapfrogging. Keywords: Artificial Intelligence, Global Value Chains, Upstreamness, Africa, Absorptive Capacity, Service-Led Development. *Notice: All the tabs, visualizations, references and futher information and details are included inside the submitted paper. Introduction The African continent has long been portrayed as a latecomer to industrialization, yet it has repeatedly demonstrated its ability to leapfrog technological stages - most notably through the mobile revolution that bypassed traditional landline infrastructure (Aker and Mbiti, 2010). However, unlike mobile telephony, which primarily connected people AI, has the potential to re-organize?reshape the global production itself. By drastically reducing coordination costs, AI enables what Baldwin (2016) terms the "Third Unbundling"; i.e the possibility of transmitting complex tasks across borders in real-time, severing the historical link between production location and producer nationality. For a continent that seeks to move beyond commodity extraction, this presents a historic opportunity: the chance more than ever to integrate into Global Value Chains (GVCs) not through low-cost assembly, but through high-value services, a pathway known as service-led development (Hallward-Driemeier and Nayyar, 2017). Yet, this opportunity is shadowed by a significant risk. The same technologies that enable tele-migration could also automate the very tasks which developing countries rely on for entry into GVCs, leading to what some have named/termed "premature automation" (Brookings Institution, 2025). The central question therefore, is whether AI preparedness acts as a catalyst, pulling African economies upstream toward higher-value activities, or as a barrier, trapping them in downstream segments or even excluding them entirely. Recent advances in trade economics now allow us to measure this positioning with precision. Mancini et al. (2023) provide granular indicators of "upstreamness" i.e the distance of a country-industry pair from final demand -enabling researchers to track whether an economy is moving towards the conceptual design stages (upstream) or is remaining closer to assembly and final consumption (downstream). To date, however, we have no study that has linked these positioning metrics to the specific capacities needed to harness AI. This paper fills this gap. We construct a novel dataset that combines, for the first time, the GVC upstreamness indicators from Mancini et al. (2023) with the IMF's AI Preparedness Index (AIPI) for a balanced panel of 11 African economies over the period 2000 to 2022. To address the critical issue of cross-sectional dependence, inherent in interconnected economies, we employ Common Correlated Effects (CCE) and Interactive Fixed Effects (IFE) estimators (Pesaran, 2006; Bai, 2009). Our results reveal a nuanced and policy-relevant reality. First, in our chosen specification that fully accounts for latent common factors, we find that AI preparedness in isolation is associated with a significant decrease in upstreamness (β = -0.042, p < 0.001). This suggests that, without complementary assets, AI adoption may push African economies toward less complex, more routinized segments of GVCs - consistent with a "barrier" or "premature automation" narrative. Second however, we find that this effect is conditional on absorptive capacity: the interaction between AI and human capital reverses this negative trend, indicating that AI acts as a powerful complement to (not a substitute for), an educated workforce. Third, our analysis reveals that Foreign Direct Investment (FDI) positively contributes to GVC upgrading, only once common shocks are controlled for, highlighting/underscoring that the quality of investment (knowledge transfer) matters more than its quantity/intensity. This paper makes three main contributions to the literature. First it provides the first empirical test of the AI-GVC nexus in Africa using granular positioning data, moving beyond speculative or case-study evidence. Second, it methodologically advances the literature by demonstrating the importance of accounting for cross-sectional dependence in African panels, where countries are subject to common global technological and commodity price shocks. Finally, it tends to offer concrete policy insights: AI is not a magic bullet for leapfrogging; its benefits are contingent upon consistent investments in human capital and the strategic management of FDI to include knowledge-transfer clauses, ideally coordinated at the continental level through frameworks like the African Continental Free Trade Area (AfCFTA). The remainder of the paper is organized as follows. Section 2 reviews the relevant literature. Section 3 describes the data and presents stylized facts. Section 4 outlines the empirical strategy. Section 5 discusses the main results and robustness checks. Section 6 concludes with policy implications. 2. Literature Review This section reviews the existing literature on Africa's integration into Global Value Chains (GVCs) and the potential disruptive impact of Artificial Intelligence (AI). It is structured around three interconnected pillars: first, the foundational literature on measuring structural positioning within GVCs; second, the literature framing AI as a potential catalyst for leapfrogging and service-led development; and third, the emerging literature warning of AI as a new barrier to entry, leading to technological bifurcation and premature automation. The synthesis of these streams reveals a critical gap that this study aims to address. 2.1. Positioning in Global Value Chains: From Measurement to Meaning A country's role in international production fragmentation is not merely about participation but about position. Seminal work by Fally (2012) and Antràs et al. (2012) introduced the concepts of "upstreamness" and "downstreamness" to quantify a sector's distance from final demand or from primary factors of production. As formalized by Antràs and Chor (2013, 2019), an upstream industry sells a large share of its output as intermediate inputs to other sectors, placing it further from the final consumer, while a downstream industry is more proximate to final demand. These measures provide a powerful lens through which to analyze a country's specialization within the vertical chain of production. Building on these theoretical foundations, Mancini et al. (2023) make a significant empirical contribution by constructing a comprehensive, globally harmonized dataset of GVC positioning indicators. By drawing on multiple Inter-Country Input-Output (ICIO) tables (WIOD, OECD TiVA, EORA), they enable researchers to observe the trajectory of countries and industries over time. A key insight from their work is that economic upgrading is not synonymous with a unidirectional move upstream. Instead it involves specializing in segments, where value capture is maximized, whether through innovation and design in upstream activities or through the provision of complex, high-value services in downstream segments. This nuanced understanding of positioning provides the essential analytical toolkit for this paper, allowing us to move beyond simple participation metrics. 2.2. The Third Unbundling and the Promise of Service-Led Development A parallel and more recent body of work posits that digital technologies, and AI in particular, could fundamentally alter the geography of production. Baldwin (2016, 2019) articulates this through the concept of the "Third Unbundling." If the First Unbundling saw production separate from consumption (driven by falling transport costs) and the Second saw factories separate from management (driven by falling communication costs), the Third, involves the unbundling of tasks themselves. By drastically reducing coordination costs and enabling "tele-migration," AI allows complex cognitive tasks - previously tethered to high-cost locations - to be performed remotely, potentially integrating developing-country talents into global value chains without physical relocation. This technological shift opens the door to a "service-led development" pathway (Hallward-Driemeier and Nayyar, 2017). The classical model of industrialization through low-cost manufacturing, may no longer be the sole/main route to prosperity. Instead, count