Evaluating Investment Risks in LATAM AI Startups: Ranking of Investment Potential and Framework for Valuation

TL;DR

A case study with the Total Addressable Market, Serviceable Available Market, and Serviceable Obtainable Market metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method is explored.

Abstract

The growth of the tech startup ecosystem in Latin America (LATAM) is driven by innovative entrepreneurs addressing market needs across various sectors. However, these startups encounter unique challenges and risks that require specific management approaches. This paper explores a case study with the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method. By analyzing key emerging powers such as Argentina, Colombia, Uruguay, Costa Rica, Panama, and Ecuador, the study highlights the potential and profitability of AI-driven startups in the region through the development of a ranking of emerging powers in Latin America for tech startup investment. The paper also examines the political, economic, and competitive risks faced by startups and offers strategic insights on mitigating these risks to maximize investment returns. Furthermore, the research underscores the value of diversifying investment portfolios with startups in emerging markets, emphasizing the opportunities for substantial growth and returns despite inherent risks.

Figures (7)

• Figure 1: Comparing basic infrastructure elements needed to develop and implement AI in the Latin American region. Data for electricity access was sourced from the World Bank Group, access for internet access was sourced from the World Bank Group and the UNDP, and access for speed internet was sourced from the Speedtest Global Index.
• Figure 2: Cloud ecosystem elements needed for AI development and implementation. The Global Cloud Ecosystem Index was sourced from MIT, the number of cloud ecosystems was sourced from Tele Geography, the number of cloud services was sourced from Data Center Map, and HPC systems distribution in LATAM was sourced from RISC2.
• Figure 3: Distribution of the TOP 100 universities in Latin America. Data was sourced from the QS World University Ranking.
• Figure 4: Comparing academic levels in LATAM through the research and patents status in the region. Data was sourced from the Emerging Technology Observatory.
• Figure 5: Research and development expenditure made in Latin America for the years 2019-2020. Data was sourced from the World Bank Group.