Why AI Keeps Designing Bad Trips (And Why Most Travel Itineraries Fail—At First)
Let’s be honest.
If you’ve ever tried to plan a multi-day trip that actually covers an entire state—trying to understand Montana beyond Glacier National Park, or Tennessee beyond Nashville—you’ve hit The Wall.
The Wall is that moment when you have 45 browser tabs open. Five of them are identical “Top 10 Best Things” SEO-bait articles. Three are TripAdvisor threads from 2018 arguing about barbecue. The rest are Google Maps tabs, zoomed in and out endlessly, trying to determine whether the World’s Largest Ball of Twine is actually on the way to anywhere useful.
It’s exhausting.
You’re not planning a trip.
You’re drowning in unorganized data.
And this is where most travel itineraries quietly collapse.
Why Most Travel Itineraries Fail
Most travel itineraries fail for a simple reason:
They confuse information with structure.
Lists are not plans.
Maps are not strategies.
And AI—left unsupervised—will happily generate beautiful nonsense at scale.
Modern travel advice looks abundant:
- “Top 25 things to do”
- “Perfect 5-day itinerary”
- “Hidden gems locals don’t want you to know”
But abundance isn’t freedom.
It’s cognitive overload wearing a smile.
Most travelers unknowingly inherit three broken assumptions:
- More options = better trips
- Famous places = essential places
- Efficiency = value
None of these hold up in real travel.
What actually happens is predictable:
- Days get overstuffed
- Distances are underestimated
- Fatigue accumulates
- Decision quality drops
- The trip quietly becomes something to recover from
This isn’t a failure of motivation.
It’s a failure of design.
Why “Just Ask AI” Doesn’t Work
AI didn’t invent bad itineraries.
It inherited them.
When you ask an AI:
“Plan me a 7-day trip to Arizona”
You’re giving it:
- No spatial logic
- No pacing constraints
- No definition of what matters to you
- No rules about tradeoffs
So it does what it’s trained to do:
- Aggregates popular attractions
- Compresses them into neat-looking days
- Optimizes for completeness instead of coherence
The output looks confident.
It reads well.
It fails the moment rubber meets road.
AI is extremely good at filling containers.
It is terrible at choosing containers for you.
That distinction is everything.
If you simply ask ChatGPT, Claude, or Gemini for a “perfect 5-day itinerary,” you’ll get the same generic Top-10 list—just regurgitated by a robot.
This series is not about that.
The Real Problem: We Plan Trips Backwards
Most people start travel planning at the wrong end.
They begin with:
- Attractions
- Activities
- Photos they’ve seen online
But trips don’t fail at the attraction level.
They fail at the structural level.
Before asking:
“What should I see?”
You must first answer:
- How is this place spatially organized?
- Where are the natural pause points?
- What supports multi-day staying instead of constant movement?
- Where does energy regenerate instead of drain?
This is not inspiration work. It’s systems work.
And it’s the missing layer in almost every itinerary you’ve ever seen.
From Bucket Lists to Spatial Thinking
A place—whether a state, country, or region—is not a flat surface with pins on it.
It’s a network.
Some places pull you in and hold you.
Others are pass-throughs.
Some deserve days.
Others deserve a stop and a coffee.
Traditional itineraries ignore this hierarchy.
Instead, they flatten everything into:
“Day 1 / Day 2 / Day 3…”
That format hides the most important question:
Where does this trip actually live?
When you skip that question, you end up with scattered dots on a map and no logical flow—cool museum here, nice hike there—until you realize you’ve built a travel problem, not a travel experience.
Welcome to AI Travel Architecture
This article—and a future YouTube series—is the manifesto behind a different approach.
We are not here to tell you where to go.
We are here to teach you how to engineer the prompts that let you design your own perfect trip, anywhere in the USA. Or in the world, if you want.
We are moving from being passive consumers of “Must-See” lists to active architects of our own adventures.
This is why the real breakthrough in travel planning isn’t better destinations.
It’s better architecture.
The “Warts and All” Promise
Before we go further, a reality check.
AI is incredible—but it’s also like an extremely confident, slightly drunk intern.
- It hallucinates.
- Sometimes it suggests driving six hours in the wrong direction for a mediocre sandwich.
- Sometimes it insists a local dive bar is a “UNESCO World Heritage candidate.”
And that’s okay.
The goal here isn’t push-button perfection.
The goal is learning prompt engineering.
When the AI screws up, we don’t edit it out or hide it.
We laugh at it, analyze why the prompt failed, and engineer a better one together.
We’re learning a new skill.
Mistakes are part of the process.
Why Architecture Comes Before Itineraries
In this series, everything begins with a simple shift:
We do not plan trips.
We design travel systems.
That means:
- Defining anchor hubs before attractions
- Enforcing spacing before listing highlights
- Creating structure before adding flavor
This isn’t about rigidity.
It’s about reducing invisible friction.
When structure is right:
- Days feel lighter
- Decisions feel obvious
- Slowing down happens naturally
- Exploration replaces optimization
Good trips don’t feel efficient.
They feel inevitable.
The 10,000-Foot View: The 4-Prompt Framework
Over the course of this series, we’ll master a four-stage prompt engineering process. Think of it like building a house: foundation, framing, rooms, and finally decoration.
Phase 1: The Anchor Lattice (The Skeleton)
Goal: Define the state’s tourist geography.
You cannot understand Texas by only looking at Dallas and Houston. You need the secondary and tertiary hubs that define the regions.
- Prompt 1 identifies a logical lattice of anchor cities and towns from a tourism perspective.
Phase 2: The Expansion (Filling the Gaps)
Goal: Avoid flying over the flyover country.
- Prompt 2 forces expansion beyond major hubs to include rural gems, scenic byways, and the in-between spaces.
Phase 3: The 7 Pillars (The Organization)
Goal: Stop data overwhelm.
- Prompt 3 organizes attractions into seven standardized travel categories, turning chaos into a usable menu.
Phase 4: The Personal Build (The Itinerary)
Goal: A daily plan that actually works for you.
- Prompt 4 applies your preferences to the structured system built in phases 1–3, producing a coherent, humane itinerary.
Only at the very end do we talk about “what you like.”
What Episode 1 Actually Sets Up
This article (Episode 1) does not teach you how to plan a trip.
It teaches you why you shouldn’t—yet.
It reframes travel planning as:
- A design problem, not a scavenger hunt
- A spatial problem, not a popularity contest
- A human-energy problem, not a logistics spreadsheet
Everything that follows—the anchor lattices, category systems, and AI prompts—rests on this foundation.
Skip this step, and AI will faithfully automate the wrong thing.
The Quiet Promise of This Approach
This architecture does something subtle but powerful:
- It shifts pressure away from a few overexposed places
- It rewards staying instead of rushing
- It turns travel into learning instead of consumption
Most importantly, it gives travelers agency.
Not:
“Tell me where to go.”
But:
“Help me design a trip that fits how I move through the world.”
That’s not automation.
That’s augmentation.
What Comes Next
In the next post (Episode 2), we stop critiquing broken systems and start building a new one.
We begin with Anchor Hubs—the gravitational centers that quietly determine whether a trip feels expansive or exhausting.
Before then, open your favorite AI tool and ask it a simple question:
“What are the major tourist hubs in my state?”
Notice what it gets wrong.
Notice what it misses.
Notice what feels off.
Bring those observations with you.
The itinerary doesn’t start with Day 1.
It starts with where you stand still long enough to look around.
That’s where real travel begins.
