James Hardaway, 38, a manufacturing manager in Texas, has a reputation in his circle as the “reviews guy.” Before he buys something big, he checks Wirecutter and Consumer Reports, does a quick scan of Reddit, and then reads whatever the internet says on YouTube. He even treats Rotten Tomatoes scores and media writeups as part of the workflow when new movies drop. The logic is simple: homework should help him spend better.

But the receipts are messier. He and his wife spent $450 on what they found was the “best” stroller when their first child was born. They swapped it out for cheaper options within months. It was not an isolated moment either. Hardaway is “not in love” with the internet-approved dishwasher or mattress, though he does like a noise machine that earned approval online. For him, the whole system leaves a lingering question every time he clicks “buy”: did he just overpay for perfection that probably did not exist?

That uneasy feeling is not just personal. The broader pattern is that many Americans increasingly treat products, services, and experiences like a research project. The source points to Pew Research Center data from 2016: eight in 10 Americans read online reviews before buying something for the first time. And the habit appears to be deepening. The piece cites a 2026 Yelp questionnaire finding that an “overwhelming majority” of Gen Z consumers research on at least one platform before picking a restaurant, gym, or beauty salon.

Reviews are, in theory, a breakthrough. They let you tap into the wisdom of the crowd instead of relying only on brand marketing or a traditional critic. But the volume and visibility create a new trap: you do not just find information, you get pulled into endless comparison. The article describes a familiar scene. You decide on pizza, browse local options, and after 30 minutes you might have 15 possibilities and dozens of reviews, yet you are still no closer to a decision. The choices are not necessarily worse. The process is just taking over your evening.

This is the paradox the researchers warn about. Barry Schwartz, a professor emeritus in social theory and social action at Swarthmore College and the author of “The Paradox of Choice,” is quoted as saying, “Once you're out for the best, the path to misery is pretty straight.” His point: the problem is not reviews. It is the escalation of the goal, the idea that there must be a “perfect decision” if you keep scrolling. Michael Luca, a professor at Johns Hopkins Carey Business School, adds a practical framing: “When do we need to be optimizing? When do we need to be satisficing? And just finding something that works for what we're looking to do?” In plain English, you may be spending time optimizing for a hypothetical ideal when “good enough” would do the job.

The internet intensifies the instinct to over-optimize because of how platforms surface information. Google often highlights stars before you even see the website. Amazon shows reviews before the price. AI search engines increasingly summarize the online consensus before users click through. For businesses, that means reviews are no longer just feedback after purchase. They are a product discovery layer, sometimes make-or-break for small companies that “live and die” by online rating systems and algorithms.

Layer on anxiety, and you can get decision paralysis. The article gives the pizza example again, but with the endgame: inundated or dispirited, a person may give up on pizza entirely and make a sandwich instead. That is an opportunity cost, not just a mood. And it becomes more complicated because tastes vary, and you often do not know anything about who wrote the review. Schwartz is quoted explaining the oddness of the authority we borrow from strangers: “You're flipping coins because you don't know the source of any of these reviews, but somehow it gives your decision a certain authority that otherwise it wouldn't have.”

Then artificial intelligence enters the chat and changes the rules. Some platforms use AI to summarize reviews, compressing hundreds of opinions into a handful of sentences. The piece notes two practical effects. First, summaries can help consumers get a general sense of feedback, making review sections feel more useful. Second, they can break down for “power users” who want the full detail. And there is a specific mismatch in the cited research: AI summaries increase purchase intention for products like electronics and appliances, but they do not move the needle as much for experiences like restaurants and hotels.

There is also a risk that the summary is simply wrong or incomplete. Luca is quoted warning that AI can produce “one-size-fits-all” summaries that do not necessarily fit individual preferences. The article also includes a more technical explanation from Doug Straton, chief marketing officer at Bazaarvoice, a software company that helps brands collect and display ratings and reviews. He says when AI platforms crawl the web for reviews, they tend to look at volume, recency, and average rating to decide which products the bot talks about, but not necessarily the context of what those opinions actually mean. He gives a concrete example: a reviewer might dislike a scent such as cucumber, a bodywash could get a negative rating for that reason, and an AI reader may only see “smells bad” without the context.

Finally, there is the regulation-and-trust angle executives cannot ignore: fakery. The article describes AI as making it easier to spin up fake reviews and harder to detect what is AI-generated. It cites Kay Dean, a former federal criminal investigator and founder of Fake Review Watch, an industry watchdog, who says fake reviews have long been a “massive” problem and that platforms are not aggressive enough in cracking down. Dean is quoted saying, “They sweep it under the carpet.” The piece also mentions that fake reviews are part of a broader ecosystem, including barter-and-trade tactics, review broker schemes, and a “whole cottage industry” and “robust black market” for review providers. The Federal Trade Commission is referenced in the source, but the excerpt cuts off before details are provided.

So what does this mean for decision-makers, boards, and operators? Reviews are supposed to reduce uncertainty, but in the story, they amplify second-guessing. AI summaries may reduce time spent reading, yet they can also flatten nuance and reduce consumer confidence when the “perfect” pick still turns out disappointing, like the $450 stroller. And for businesses whose discovery depends on stars and algorithms, fake or miscontextualized reviews can distort the signal that underpins revenue. The executive stake is not whether reviews exist. It is whether the review layer stays a helpful feedback loop or turns into a trust crisis that pushes customers from researching to disengaging.