Every few months a tech publication runs some variant of "ChatGPT will replace travel agents." The argument sounds airtight: travel planning is mostly research, LLMs are great at research, therefore the job is done.
I work as a backend developer at MindDMC, an AI itinerary platform built for travel agents and DMCs. When the team started, we tried to build the entire product on top of GPT-4. That approach failed — not because the model was not smart enough, but because we were trying to solve a transactional B2B problem with a generative consumer tool.
The architectural mismatch was the lesson. I think it is worth sharing because the same pattern shows up in healthcare, legal tech, financial services, and any other domain where AI gets pitched as a replacement for human professionals.
This is a technical post about why the architecture of consumer LLMs makes them structurally incapable of doing what a travel agent does — and what a system that can do that job actually needs to look like.
The demo trap
