I Paid for 2,150 Survey Responses. Then I Threw 441 of Them Away.

I wanted to do some original research into how people actually think about AI companions.

There are already plenty of reviews and affiliate sites in this space. I was interested in something different: the people using these products.

Why do people use AI companions? What do they actually think about them? Where do they draw the line between an AI interaction and a real relationship?

My thinking was pretty simple: if I could understand even a little more about how people view these products, I could write better reviews and potentially provide more useful feedback to the developers building them.

So I decided to spend my own money on original research.

I paid for 2,150 responses

I commissioned a survey of 2,150 U.S. adults through SurveyMonkey Audience.

The questions covered things like AI companion usage, loneliness, romance, emotional attachment, privacy, and whether having a romantic relationship with an AI could count as cheating.

Then I got the data back.

At first, some of the results looked incredible.

And I don't necessarily mean that in a good way.

While analyzing the responses, I started noticing a pattern. When I looked at individual respondents, a large group seemed to be repeatedly selecting the first available option.

Again and again.

It looked less like 441 people who happened to have remarkably similar opinions and more like people trying to finish the survey as quickly as possible and get paid.

My first thought was basically:

I just wasted my money.

I had paid for honest answers. Apparently, some of the respondents didn't have quite the same idea.

I considered keeping them

I don't want to pretend I immediately took the high road and deleted them.

I actually considered keeping them.

After all, I paid for 2,150 responses.

And the unfiltered results gave me some crazy findings.

Before removing the suspicious responses, 30.3% of respondents appeared to be current AI companion users. More than a quarter, 27.1%, said they could genuinely fall in love with an AI.

Those are fantastic numbers if your goal is to write headlines.

And technically, I could just report what the survey returned. I wouldn't have to invent anything. I could say these were the results and leave it at that.

But I knew there was a problem.

The suspicious group wasn't subtle. Of those 441 responses, 98% came from Android devices. Their median completion time was only 39 seconds, and they arrived within a concentrated 2.5-hour window.

So eventually I accepted that keeping them would defeat the entire reason I commissioned the survey in the first place.

I wasn't trying to manufacture the most exciting statistics possible.

I was trying to understand this niche.

So I removed all 441.

And some of my best numbers disappeared

That left me with 1,709 responses.

Suddenly, the percentage of current AI companion users dropped from 30.3% to 12.3%.

The percentage saying they could genuinely fall in love with an AI dropped from 27.1% to 8.3%.

Those aren't small corrections.

The dataset told a substantially different story once the suspicious responses were removed.

It hurt a little. 1,709 is still a large sample, but I paid for 2,150. That's my hard-earned money sitting in those 441 discarded rows.

Eventually, though, I started looking at the loss differently.

Documenting what happened and explaining why I removed those responses probably adds more credibility to the research than pretending I never noticed the problem.

The research became more useful than I expected

I published the study through AI Girlfriend Coach, the site I'm building around AI companion testing and research.

Since then, I've been able to take the remaining data in quite a few directions.

The research has been picked up by other publications, and I've seen people use the findings to develop their own insights about AI relationships.

I also learned that original research doesn't have to replace the rest of what I'm doing.

I'm still reviewing AI companion platforms. But now I'm also giving specific feedback to developers, preparing research PDFs for companies that want them, and creating guides based on what I'm learning about the people actually using these products.

For me, those things complement each other.

Understanding the product is useful.

Understanding the user is useful.

Putting the two together is much more interesting.

What I'd do differently next time

I'd spend considerably more time planning before commissioning another survey.

Not just writing the questions, but thinking carefully about the target population, quality controls, what I actually want to learn, and how I'll evaluate the responses once they arrive.

My biggest lesson is probably that paying for original data doesn't automatically give you good original data.

And sometimes the most valuable thing you can do with data you've paid for is throw some of it away.

Has anyone else here commissioned surveys or other original research for a product? I'd be interested to hear how you handled response quality, especially when using third-party panels.