We’ve all seen the demos.
A user chats with an AI agent and gets a perfectly planned vacation.
The interface is magical. Seamless. Effortless.
But here’s the real question:
What is happening behind the scenes — and who is paying for it?
Spoiler: It’s not the AI company. It’s you.
🧠 AI Is Not Replacing Software — It’s Replacing the User
There is a growing misconception in travel tech:
“AI will replace the booking engines.”
No.
AI does zero availability computation.
LLMs cannot evaluate millions of rate combinations or room contracts.
What AI does is:
- interpret user intent
- break it into multiple sub-questions
- call booking APIs repeatedly
- ask for more data until it can confidently answer
This means AI replaces the user, not the travel stack.
The backend still does all the work — just 5–10× more of it.
🔍 Welcome to RAG: Retriever-Augmented Generation
When an AI agent tries to answer:
“Show me hotels in Barcelona under €150 with a sea view and free cancellation.”
It doesn’t send one availability request.
It sends many:
- retrieve price buckets
- retrieve cancellation policies
- retrieve board types
- retrieve room-level metadata
- check availability again to confirm
- re-filter after user clarification
- repeat steps for multiple suppliers
This is the RAG pipeline:
Retrieve → Reason → Retrieve → Answer.
📈 The impact:
A search that used to cost $0.01 now easily becomes $0.05–$0.10.
Multiply by thousands of RAG calls per hour, per OTA, per partner…
And suddenly your infrastructure cost explodes — not the AI company’s.
🤖 AI Has Lowered the Bar for Crawlers
Even if your platform is not using RAG internally, you’re still affected.
Why?
Because AI tools make screen scraping and crawling far easier. Anybody can vibe code a crawler in a few hours.
Competitor intelligence startups can now spin up:
- price crawlers
- rate-parity compliance bots
- availability testers
- optimization scripts
- content diff bots
…with almost no engineering.
Before LLMs, building these tools required real engineering talent.
Now:
- GPT writes your crawler
- Cloudflare proxies it
- A $5/month server runs it
- A new “competitive intelligence company” appears every week
Your search API becomes the data source for dozens of third parties —
many of whom you will never know about.
Result:
Your infrastructure is working harder than ever,
for people who are not even your customers.
💣 The Hidden Cost Explosion in Travel Distribution
Let’s summarize the problem:
AI agents multiply search traffic.
One user → many backend calls.Crawlers and scrapers multiply traffic further.
Zero friction to build, cheap to run.Legacy booking engines were not designed for multi-agent LLM usage.
They choke under unpredictable load.Search is already the most expensive part of travel distribution.
AI turns an expensive operation into a very expensive one.Most systems pay $50–$100 per million searches.
With RAG, that becomes $300–$700.Who pays?
Bedbanks. Consolidators. OTAs. Suppliers.
Everyone except the AI companies.
🧵 The New Problem AI Creates (That Nobody Is Talking About)
Travel is one of the few industries where:
- results depend on real-time data
- prices change constantly
- availability shifts instantly
- suppliers and contracts are complex
- merging/dedupe is computationally heavy
LLM interfaces don’t simplify this.
They stress the infrastructure harder.
AI creates a backend problem — not a UI problem.
And this is exactly why efficient, low-cost, ultra-fast availability engines will decide which travel companies survive.
📌 Final Thought
AI looks magical.
But someone is paying the price for the magic.
Today, that someone is the bedbank, wholesaler, or OTA running the availability API.
Search cost is becoming the biggest hidden expense in travel.
AI is accelerating it.
And the industry isn’t prepared.
In future posts, I will explain:
- why AI-era search load increases 5–10×
- how to reduce cost per million search by 100×
- how to build RAG-friendly availability caches
- and why most travel systems will break under AI load
Stay tuned.