Jovan Kis, a Co-Founder @ UkisAI (Building TrueStay), recently posted something that made me stop scrolling: a friend asked him about a hotel in Paris for a birthday trip with his girlfriend, shared two options rated 8.1/10 on Booking, and Jovan replied, "Wait 2 minutes." Then he surfaced deal-breaking details hidden inside the reviews.

"A friend asked me about a hotel in Paris... Coming with my girlfriend for her birthday." Two options. Both 8.1/10. I said: "Wait 2 minutes." AI read 1,247 reviews in 30 seconds.

That small story captures a travel problem most of us have felt: rating averages are convenient, but they can be misleading. Two hotels can look identical on a platform, while the lived experience is wildly different once you read what people actually say.

In Jovan's example, Hotel #1 had 700+ reviews and included comments like "We came back bitten, BEDBUGS" and "The area isn't pleasant to walk at night." Hotel #2 had 500+ reviews and complaints like "No space for smoking" and "The metro station is a 15-minute walk." Same average score, completely different risk profile.

What resonated with me is not just the product moment, but the decision-making lesson: the best travel choice is rarely about the number. It's about the pattern behind the number.

Why two hotels with the same score can feel worlds apart

Most booking platforms collapse a complex set of experiences into one tidy metric. That is helpful for browsing, but it introduces three common distortions:

1) Averages hide extremes

A hotel with a serious, rare issue (bedbugs, safety concerns, consistent noise) can still maintain a decent average if plenty of guests had an "okay" stay. The average does not tell you whether the downside is catastrophic.

2) Different guests care about different things

One person gives 10/10 because the lobby is pretty. Another gives 6/10 because they could not sleep. The average blends incompatible priorities.

3) Recency and context get lost

If the elevator was broken for two months, or a neighborhood changed, the signal is in the timeline and the wording. Most travelers do not have time to read enough reviews to detect that.

Jovan's point was simple: the truth is in the text, but the text is too slow for humans to process at scale.

What AI review scanning actually changes

When Jovan says, "AI read 1,247 reviews in 30 seconds. I just pasted the link," the value is not magic. It's compression.

Instead of you spending 40 minutes doom-scrolling reviews, AI can:

Extract recurring themes (cleanliness, noise, safety, staff, location)
Identify high-severity risks (bedbugs, theft reports, harassment, mold)
Separate "nice-to-have" complaints from "do-not-book" warnings
Summarize tradeoffs in plain language

That shift matters because travel decisions are often time-boxed. You are booking between meetings, or while juggling flights, or late at night. Under time pressure, humans default to easy proxies like average rating and price. AI can re-introduce nuance without re-introducing effort.

A practical framework: turn review text into a decision

If you want to apply the idea from Jovan's post even without any particular tool, here is a simple approach that mirrors what an AI summary should help you do.

Step 1: Define your "non-negotiables" before you look

Pick 3 to 5 items that, if true, would ruin the trip. Examples:

Clean bed and bathroom (no pests, no mold)
Personal safety when returning at night
Quiet enough to sleep
Elevator if you have luggage or accessibility needs
Proximity to transit under 7-10 minutes walking

This prevents you from getting distracted by less important details.

Step 2: Look for severity, not frequency alone

Some issues are "annoying" (small room, weak coffee). Some are "trip-ending" (bedbugs, unsafe area, repeated theft reports).

A single credible report of something severe can outweigh dozens of vague positives, especially if the review includes specifics (dates, room numbers, photos, consistent narrative).

Step 3: Cluster complaints into themes

A powerful use of AI is grouping. For example:

Cleanliness cluster: dirty towels, smell, stains, pests
Noise cluster: street noise, thin walls, construction
Location cluster: far from metro, sketchy streets, poor lighting
Service cluster: rude staff, lost bookings, slow check-in

When multiple independent guests describe the same thing in different words, you are looking at a real operational pattern.

Step 4: Translate themes into a "risk vs tradeoff" decision

In Jovan's Paris story, the decision is easy:

Bedbugs and unsafe-feeling area = high risk
No smoking area and a longer metro walk = manageable tradeoff

The problem is that many travelers only discover those categories after they arrive.

Why "8.1/10" is not a decision

Jovan's post exposes a hard truth: review platforms are optimized for browsing, not for protecting you from edge cases.

A rating is a shortcut, but a trip is not an abstract score. You do not sleep inside an average. You sleep inside a room.

This is where AI summarization is most useful: not to replace your judgment, but to focus it on what matters.

Where tools like TrueStay fit in

Jovan shared a straightforward workflow: paste a link, let AI read the reviews, get an answer. He also emphasized the frictionless part: "Free. No registration. Just an answer" and pointed people to https://truestay.me.

Whether you use TrueStay or another workflow, the product idea is clear:

Input: a hotel listing URL
Processing: read a large sample of reviews quickly
Output: condensed pros, cons, and red flags you can act on

That is especially valuable when:

You are choosing between two similar ratings
You are booking for a special occasion and cannot risk a bad stay
You are traveling to a new city and do not know which neighborhoods feel safe at night

The LinkedIn content lesson in Jovan Kis's post

Beyond travel and AI, there's also a content strategy takeaway worth naming because it explains why the post performed well.

1) It starts with a relatable story

"A friend asked me about a hotel in Paris" is an instant hook. No jargon. No abstract promise.

2) It uses concrete, high-contrast examples

Bedbugs versus a 15-minute walk to the metro is a stark comparison. Readers do not need to be travel experts to understand the stakes.

3) It demonstrates value in one screenshot-sized moment

"Wait 2 minutes" communicates speed. "AI read 1,247 reviews in 30 seconds" communicates scale.

4) It ends with a simple call to action

"If you're traveling, try it and share the result" is low effort and naturally invites comments.

If you're building in public, this is a strong template: show the before (confusion), show the after (clarity), then invite others to test.

The bigger point: AI is best when it upgrades attention

Jovan Kis is not arguing that humans should stop thinking. He is arguing that humans should stop wasting attention on mechanical scanning.

Let AI do the reading. You do the choosing.

And if an AI summary helps you avoid one "8.1/10" hotel that hides a serious problem, it has already paid for itself in stress you never had to experience.

This blog post expands on a viral LinkedIn post by Jovan Kis, Co-Founder @ UkisAI (Building TrueStay). View the original LinkedIn post →