Jovan Kis, Co-Founder @ UkisAI (Building TrueStay), recently shared something that made me stop scrolling: "I booked a hotel rated 8.5/10 on Booking. WiFi didn’t work. The windows didn’t open. And ‘5 minutes from the center’ was actually 25. Everything was in the reviews, but on page 47."

That story is painfully relatable because it exposes a modern travel paradox: we have more information than ever, yet we make worse decisions when the signal is buried inside hundreds of comments. Jovan’s point was not that people fail to leave reviews. It is that there are 800 of them, and almost nobody has the time (or patience) to read all of them before paying.

The problem is not missing information. The problem is hidden information.

In this post, I want to expand on what Jovan highlighted: why review overload makes us overconfident, what actually goes wrong in the booking flow, and how AI review summarization (like TrueStay) can turn scattered experiences into a fast, decision-ready answer.

The real enemy is review overload, not bad hotels

Most of us treat an 8.5/10 score like a guarantee. It feels objective, compact, and safe. But aggregated ratings are blunt instruments. They average out problems that are dealbreakers for some travelers and irrelevant for others.

If your trip depends on stable WiFi (remote work, calls, uploads), a single recurring complaint like "WiFi drops every evening" matters more than the overall score. If you are sensitive to noise, "thin walls" is not a minor detail. If you travel with kids, "no elevator" is a major operational issue.

The issue Jovan ran into is classic: the crucial sentence exists, but it is located in a place humans do not reliably reach. Page 23. Page 47. The graveyard of truth.

Why we do not read the reviews that matter

A few forces stack up against us:

Cognitive overload: 800 reviews is not information, it is a project.
Recency and ranking bias: platforms surface a subset, but not necessarily the subset aligned with your priorities.
Optimism bias: we believe the top score protects us from edge cases.
Time pressure: travel planning often happens late at night or between meetings, when our tolerance for digging is low.

So we skim five comments, see a high rating, and click "Reserve." Then the WiFi fails, the windows do not open, and the "close to center" claim becomes a daily 25-minute tax.

What matters most is not the score, but the pattern

Jovan’s examples are important because they are not exotic. They are the exact kinds of issues that repeat across properties and ruin trips in predictable ways:

Connectivity that is great in the lobby and unusable in rooms
Rooms that look modern in photos but have poor ventilation
Location descriptions that depend on optimistic walking times
Safety that varies by street, not by neighborhood label

When you read reviews manually, you are looking for patterns: repetition, specificity, and consistency across time. But doing that well requires volume and focus, which is why humans fail at it under time constraints.

One hidden sentence can cost you an entire vacation.

That is the core of what Jovan said, and it is the reason summarization is more than a convenience feature. It is a risk-reduction tool.

Where AI summarization fits (and where it does not)

Jovan introduced TrueStay with a simple pitch: paste a link from Airbnb or Booking, the AI reads all reviews, and you get a summary in about 30 seconds. No signup. No payment. Just an answer.

Conceptually, this is exactly where AI shines: compressing large, repetitive text into structured insight. But it is worth being clear about what a good summarizer should do.

What a useful review summary should include

If I am using a tool like TrueStay before I book, I want more than a generic paragraph. I want:

Issue frequency: how often do people mention WiFi problems, noise, cleanliness, bugs, scams, construction, check-in delays?
Context: is the complaint tied to specific rooms, floors, seasons, or times of day?
Severity language: do people say "slow" or "unusable"? "a bit far" or "unsafe at night"?
Tradeoffs: what do guests praise that might offset negatives (or not)?
Who it is for: business travel vs families vs couples vs solo travelers.

A summary should not only tell you what people say. It should tell you what people repeatedly experience.

The risks to watch for

AI summaries can also mislead if they are careless. A few guardrails matter:

Hallucinations: the tool must not invent claims not grounded in reviews.
Over-generalization: "WiFi is bad" is less helpful than "WiFi complaints appear in many recent reviews, mostly about room signal."
Missing minority dealbreakers: a rare but critical issue might be underweighted.

The best workflow is AI first, then selective verification: use the summary to identify the 2-3 risk areas, then skim a handful of original reviews that mention those exact topics.

A practical pre-booking checklist (powered by summaries)

If you travel often, you can turn Jovan’s lesson into a repeatable system. Here is a simple checklist I use when evaluating a property with lots of reviews:

1) Start with your dealbreakers

Write down your top three non-negotiables, for example: reliable WiFi, quiet at night, safe walking area.

2) Ask targeted questions

Jovan framed the right ones: "Is the WiFi actually good? Is the neighborhood safe? What do people really say?"

If a summarizer supports it, push even further:

"Is WiFi stable for video calls in rooms?"
"Any reports of theft, harassment, or sketchy streets at night?"
"How accurate is the walking time to the center?"

3) Look for time sensitivity

A location can change if construction starts. Management can change. A tool should highlight whether complaints are recent or historical.

4) Validate with a quick sample

Open a few reviews that mention your dealbreaker keywords. If the summary says "WiFi issues are common," confirm by reading 3-5 examples. This takes minutes, not hours.

Why Jovan’s post went viral (and what creators can learn)

Even though the product is travel tech, the post itself is a great piece of LinkedIn writing. It worked because it followed a pattern that consistently drives engagement:

A personal failure story that feels universal (we have all been tricked by ratings)
Specific details ("page 47," "25 minutes") that make it believable
A clear villain (information buried in endless reviews)
A fast, concrete solution (paste link, get summary in 30 seconds)
Low friction (no signup, free)
A share trigger ("send to someone who travels")

If you care about LinkedIn content and content strategy, this is a reminder that clarity beats cleverness. Jovan did not over-explain AI. He explained the pain, then positioned AI as the obvious fix.

The takeaway: do not book a number, book a reality

Jovan Kis’s experience is a warning against outsourcing judgment to a single score. Ratings are useful, but they are not personal. Reviews are personal, but they are not readable at scale. AI summarization bridges the gap by turning the unreadable into something you can act on before you pay.

If you are traveling soon, the most valuable habit you can build is simple: stop trusting the headline rating and start extracting the patterns. Whether you use TrueStay or another method, the goal is the same: surface the one sentence that would have changed your mind while you still have a refund button.

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