Show Notes
That quick moment at the gate when you pull up a boarding pass on your phone and scan a QR code feels routine now. It isn’t. That interaction represents one of the most successful global standards ever deployed in aviation—a shift from magnetic stripes to barcodes that saved the industry over $1.5 billion annually. But the real story isn’t the technology. It’s how an entire industry coordinated across competitors, regulators, and infrastructure to make it work. Eric Leopold spent 15 years at IATA working on exactly that kind of industry plumbing. In this episode, Eric Leopold takes us inside the machinery of aviation standards—from boarding passes to APIs to AI—and explains why the next wave of innovation won’t be limited by technology, but by data consistency, trust, identity, and industry alignment. That becomes even more important once the conversation turns to AI. The interesting question is not whether an LLM can help you shop for flights. It is whether the travel industry can build the identity, data consistency, trust networks, and commercial models needed for AI agents to actually transact on your behalf without breaking the system underneath.
What You’ll Learn The barcode boarding pass was a standards and adoption challenge, not just a scanning upgrade: Replacing magnetic stripes required industry alignment across airlines, airports, manufacturers, and regulators. IATA standards only work when multiple airlines share the same problem: A standard starts when airlines identify a common need, build support, test the technical approach, and then push for industry adoption. The old airline distribution stack was both brilliant and constrained: Long before the web, airlines had global real-time reservation infrastructure, but it was built on private networks and legacy protocols that later needed modernization. NDC emerged from the need for a common API layer: Airlines had already tested direct API distribution, but agencies would not adapt for one carrier at a time, forcing the industry toward a shared standard. AI in travel depends on data models more than demos: If the underlying entities, definitions, and relationships are inconsistent, AI systems will produce plausible but wrong answers. The aviation industry data model matters more now than when it was created: A shared semantic layer becomes much more valuable once AI agents need normalized data they can reason across. Travel intermediaries may split rather than disappear: AI could create a new model where travelers have trusted buying agents while suppliers are represented by their own selling agents. Trust, identity, and settlement are still unsolved AI-era problems: For autonomous shopping and booking to work, agents need ways to verify who they represent, enforce agreements, and resolve disputes across the network.
Time-Stamped Highlights (00:10) Eric Leopold and the Hidden Infrastructure Behind Modern Travel (02:35) Why 2005 Was a Turning Point for Aviation Technology (03:13) Designing the Barcode Boarding Pass Standard (05:56) Why Politics, Not Technology, Slows Aviation Change (08:13) How IATA Actually Creates Global Standards (10:30) From Standards to Global Implementation (13:35) The Shift from Magnetic Stripes to Barcodes (16:06) How Mobile Phones Accelerated Adoption (19:57) NDC and the Move to API-Based Distribution (24:24) Airline Websites vs Online Travel Agents (28:36) AI Enters Travel Booking (30:06) Why Data Quality Is the Real AI Bottleneck (33:27) The Problem of Data Normalization (36:22) Knowledge Graphs vs LLMs (41:04) Trust, Identity, and the Future of AI Travel Agents
Guest Eric Leopold — Founder, Threedot Eric is the founder of Threedot, a consultancy focused on the travel industry, and a board member and advisor to multiple travel companies. He spent 15 years at IATA, where he worked on some of the most impactful industry standards, including the transition to barcode boarding passes and the development of airline distribution and data mod
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