The current cost of using LLMs for inference is approximately 30 times higher than using a traditional, deterministic API for flight data. This significant cost disadvantage makes it economically unviable for AI-native challengers to replace the existing airline distribution business model.

Even if AI makes it easier to build competing software, incumbent SaaS giants retain customers due to immense switching costs. The operational disruption, retraining, and integration challenges of migrating a large organization create a powerful moat against new entrants.

As AI commoditizes user interfaces, enduring value will reside in the backend systems that are the authoritative source of data (e.g., payroll, financial records). These 'systems of record' are sticky due to regulation, business process integration, and high switching costs.

Google acquired ITA software to enter the airline distribution space but ultimately found it too difficult. They have since partnered with Amadeus, signaling the immense challenge for even the largest tech firms to replicate Amadeus's entrenched network and infrastructure.

Despite investor focus on its well-known distribution business, Amadeus's Air IT division (inventory, reservation management) now generates 50% of group profits. This less visible, mission-critical software segment is the company's most profitable and formidable moat.

While businesses accept that employees make mistakes, their expectation for software is absolute reliability. This unforgiving standard creates a durable moat for enterprise platforms that provide deterministic outcomes, a key challenge for probabilistic AI models in critical workflows.

The technical debt in airlines' aging IT systems is an opportunity for vendors like Amadeus. Experts believe airlines with obsolete internal software will be forced to upgrade and are more likely to choose a proven, scaled provider rather than build a new system from scratch.

Amadeus was formed by major airlines to create a neutral distribution system. This origin story provided immediate scale, credibility, and deep industry integration, creating a powerful competitive moat from day one that would be nearly impossible for a startup to replicate.

Alex Rubalcava argues that businesses won't replace software integral to their operations—systems of record or platforms touching money, regulation, or physical assets. The high cost and risk of failure create a strong moat against AI-driven replacements, protecting companies like Shopify and Viva.