Building Enduring AI Companies in Latin America: The Power of Process as a Moat
Why Local Process Innovation, Not Engineering, Will Define the Next Generation of AI Companies
The rapid advancement of machine learning capabilities, particularly in the realm of Large Language Models (LLMs), presents both an opportunity and an existential challenge for AI companies. As these foundation models become increasingly capable, the value of engineering effort around them diminishes - a crucial insight that should shape how we think about building lasting AI companies.
📉 Understanding the Value Curve
The reality of AI product development presents an interesting paradox: while engineering effort provides significant gains with current models (as they need constraints and guidance), these gains become less valuable as models improve. Companies investing heavily in engineering specialized solutions may find their competitive advantage eroding with each new model release. This dynamic is particularly relevant when considering how to build enduring AI companies.
The Vertical vs. Horizontal Dilemma
Currently, most AI companies are building vertical solutions - specialized tools designed for specific use cases. While this approach yields immediate results, it raises important questions about long-term sustainability. Consider a study card application: How does it maintain its value proposition when users can simply ask a more capable language model not only about study materials but potentially any topic?
This challenge becomes particularly acute as horizontal AI capabilities expand. While specialized tools may offer superior performance today, the improving capabilities of foundation models suggest that general-purpose solutions may eventually close this gap.
Current State and Strategic Considerations
At present, implementing AI agents remains costly and complex, making defined workflows the most practical approach to building products that leverage LLM capabilities. However, strategic thinking demands we look beyond current limitations to future possibilities, particularly in how we create lasting competitive advantages.
🏰 The Search for Sustainable Competitive Advantage
As models become more capable, the key question shifts from how to constrain and guide them to how to build sustainable competitive advantages that persist regardless of model improvements. This is where the concept of cornered resources becomes crucial for AI companies.
The Data Misconception
Many companies believe that exclusive access to data represents a cornered resource. This is often a misconception, particularly in the age of foundation models. Data alone, unless truly unique and irreplicable, rarely constitutes a sustainable competitive advantage. The real value lies not in the data itself, but in the sophisticated processes required to gather and utilize it effectively.
The Latin American Advantage: Process as a Moat
In Latin America, a unique opportunity emerges: the process of data gathering itself can become a cornered resource. This stems from the region's distinctive characteristics. The limited digitization across many sectors creates opportunities for companies that can bridge this gap effectively. The complex bureaucratic landscape creates natural barriers that global AI companies may be unwilling to navigate. Perhaps most importantly, building trust with local institutions and stakeholders requires deep cultural understanding and presence - something that can't be easily replicated by outside entities.
Strategic Implications for Market Entry
Global AI giants are unlikely to prioritize compliance with regional bureaucracies to access local datasets, at least in the medium term (5-10 years). This creates a window of opportunity for local companies to establish strong regional presence and develop specialized infrastructure adapted to local requirements. The focus should be on building trust capital by demonstrating long-term commitment to solving regional challenges.
Opportunities in Latin American Markets
The sectors most ready for AI transformation in Latin America share common characteristics: they typically involve high volumes of unstructured data, complex regulatory requirements, and strong relationship dependencies. Success in these markets demands not just technological prowess, but also deep understanding of local market dynamics and regulatory frameworks.
🎯 Looking Forward
The path to building enduring AI companies in Latin America requires focusing on elements that remain valuable even as model capabilities increase. Rather than competing on model engineering or constraints, companies should focus on developing unique processes for data gathering and relationship building that create lasting competitive advantages.
Success in this environment requires thinking beyond immediate technological capabilities to design systems that can evolve with advancing AI capabilities while maintaining their core value proposition. The key to building enduring AI companies in Latin America lies in creating sophisticated processes and relationships that become more valuable over time, rather than engineering solutions that may become obsolete with the next model release.
Acknowledgment: Special thanks to Ivan Hernandez for the insightful conversations that helped shape the ideas presented in this post. His perspectives on strategic moats significantly contributed to this analysis.