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Marketing & Small Business3 min read

Opinion: transparent AI loading screens boost perceived value, studies show

In 2025 major answer engines such as Claude, ChatGPT and Gemini added user interfaces that reveal which websites they are searching, which documents they opened, and which assumptions they are testing. Anthropic says the feature helps people verify answers, exposes mismatches between thinking and answers, and is simply interesting to watch.

1 source

Key points

  • 2025 updates let Claude, ChatGPT and Gemini display sources, assumptions, and reasoning, which Anthropic says serves three stated purposes.
  • 2011 Harvard study with 266 users found transparent loading screens raised perceived value by 8.1 % despite longer wait times.
  • 2022 experiments (306 car‑search, 294 dating‑app participants) showed a 7‑second “calculating results” spinner increased quality ratings of identical recommendations.

The HubSpot post argues there is a fourth motive: signaling effort. It cites a 2011 Harvard study of 266 participants using a travel‑search mockup; a transparent loading list raised perceived value by 8.1 % even when the wait was up to 60 seconds, and a later group of 118 chose the slower site with the transparent screen over a faster blank one. A 2022 study by Dimitrios Tsekouras, Ting Li and Izak Benbasat with 306 car‑search users and 294 dating‑app users found that a 7‑second spinner labeled “calculating results” increased quality ratings despite identical recommendations.

The author treats the explanations from Anthropic and OpenAI as “BS” and suggests marketers can exploit the labor‑illusion effect to make AI answers feel more trustworthy. The piece is presented as an opinion.

Full story fromHubSpot Marketing Blog · by Phill AgnewOpen source ↗

The psychology behind why AI shows it's working

HubSpot Marketing Blog · 21 September 2026

In 2025, most of the major answer engines made an almost identical update. Claude, ChatGPT, Gemini, and many others started to show what they were thinking.

Answer engines would tell the user which websites it was searching, which documents it had opened, and which assumptions it was second-guessing. According to Anthropic, the company that owns Claude, the answer engine does this for three reasons.

  1. It helps people check and trust answers.
  2. It reveals mismatches between thinking and answers.
  3. It’s simply interesting to watch.

But I think there’s a fourth reason that AI providers have purposefully hidden from us.

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The Labor Illusion

In 2011, two Harvard professors published a study in Management Science that sought to understand and challenge the idea that faster means better in a customer service context. They asked 266 participants to use a travel search site. Similar to Skyscanner or Kayak, the user could enter a destination, and the site would pull back several flights to potentially book.

However, there was a twist. One group saw just a plain loading wheel on a white background as the search engine filtered through results.

The second saw the plain loading wheel, plus a live scrolling list of which airlines were being searched, with fares visibly stacking up as they were found. Interestingly, the wait was randomly set to 10, 20, 30, 40, 50 or 60 seconds.

Once participants had seen the results, they were asked to rate the website out of seven. Turns out, the results from the loading wheel that show it’s working were perceived as 8.1% higher value than the same results from a blank loading wheel.

Incredibly, people rated the results higher even if the transparent loading wheel took longer. When the search engine showed it was working, people preferred it — even if it took 50 seconds longer to gather the same results.

Later, a new group of 118 participants was asked to pick between using a faster site with no transparent loading screen and a slower site with the transparent loading screen. The results that both sites generated were the same.

Participants were more likely to pick the slower site with the transparent search over the faster site with the blank search.

Labor looks smarter.

As with all good science, one paper from 2011 isn’t enough to draw lofty conclusions.

Fortunately, in 2022, Dimitrios Tsekouras, Ting Li, and Izak Benbasat published research in Information and Management to explore how signalling effort — even through something as simple as a spinning loader — changes how users evaluate a recommendation system.

For the first study, 306 participants were shown a search engine for cars. In a second study, 294 participants were shown an online dating app that used a loading wheel to match potential couples.

Participants were split into four groups across two variables: how much effort they had to put in upfront, and whether the system showed effort back. In the high effort conditions, participants saw a rotating loader with the words “calculating results” for seven seconds. In the low effort conditions, results appeared immediately.

The recommendations themselves were the same. The only difference was seven seconds of a spinner.

Those who saw the loader rated the recommendation agent’s quality significantly higher than those who received instant results — even though the recommendations themselves were identical.

Anthropic and OpenAI might tell us that the transparent loading screen is just there because it’s “simply interesting to watch,” but most marketers know that’s BS.

Our perception is shaped by the effort spent creating something. And most of us will prefer a slower answer engine that shows it’s working to a faster one that doesn’t.

How to Use Psychology in Marketing

Access the guide to learn more about psychology.

  • Turn customers into fans.
  • Understand Maslow's hierarchy of human needs.
  • Understand how marketing can influence how people think, feel, and behave.

This text was published by HubSpot Marketing Blog and written by Phill Agnew. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

Topics · follow one to build your own front page
AnthropicOpenAIGoogleHubSpotClaudeChatGPTGeminiDimitrios TsekourasTing LiIzak Benbasat

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