Month: April 2026

The very first YouTube video, uploaded by one of YouTube’s founders before it was purchased by Google, is called “Me at the zoo”. It was uploaded about 21 years ago. It has accumulated almost 400 million views and over 10 million comments. This is NOT the most watched YT video, which is a children’s video that doesn’t earn the creator money, with over 16 BILLION views. But that first YT video, only 19 seconds run time, noting how long the elephant trunks are, is still perhaps the quintessential example of what a good YouTube video SHOULD be, a slice of reality, a piece of true emotion.

Of course, that’s decades ago, and since then YouTube has become a massive money making machine for Google, and the quality of videos overall has plummeted to the Earth’s core. Because for Google, a click is a click, a view is a view, and Google cares little about what kind of content — other than blatantly illegal — generates those views of associated ads that now bracket and interrupt YouTube videos for non-paying viewers.

But it’s still the most important streaming platform on the planet. The technical infrastructure that supports YouTube is staggering in its size and complexity. And it’s not that there aren’t still wonderful, incredibly great videos on YouTube. There are, if you can find them. And that’s become increasingly difficult as AI-generated Slop increasingly permeates the platform, burying the good stuff under mountains of muck.

The relatively recent availability of powerful AI video generation tools has created a cosmic-sized mess. There are many types and trends in these. You almost certainly have seen them if you ever view YouTube. The previously rather robotic voices have vastly improved, but are (at least currently) still often recognizable. The happy, happy voice. The deep booming dignified voice. On and on. The quickie AI videos are churned out as rapidly and in as high volume as possible by those channels from automated text prompts since viewership of any individual video may be relatively limited.

They often have very recognizable characteristics. Slide shows with the images slowly moving around the frame. Mixes of images where some are utterly unrelated to the ostensible topic. Voices that pause in odd places or get obvious pronunciations wrong. Completely distorted images of objects or people from AI hallucinations. Lately new trends include AI videos ostensibly showing famous people saying or doing things that they may or may not have actually ever said or done. A famous deceased scientist supposedly narrating a text he wrote many years ago, sometimes including still or moving AI imagery of the scientist. Lots of images with annoying bits of what look like dirt or feathers or other debris constantly floating around over them. False or distorted histories, fake “revenge” or “confession” stories about evil Homeowner Associations, or awful bosses, or terrible spouses or other relatives. Endless numbers of such videos with minor variations. In the comments, usually some viewers are apparently taken in — “Oh, what an inspiring story!” — while other comments point out the AI Slop in very specific ways.

Keep in mind that YouTube does not reliably label as “synthetic or altered content”, even briefly, many AI-generated videos. By the way, my personal policy is to thumbs-down ANY video I run across that obviously is AI Slop.

But perhaps Google would be just fine with pretty much EVERY video on YouTube being created by Google AI — hey, no more creators to pay! After all, if viewers are willing to tolerate AI Slop, why bother presenting them with anything else? The YouTube money machine is heading toward a closed ecosystem — AI Slop flows out from YouTube, ad revenue flows in, with no room for genuine creators. YouTube may ultimately consider everything else to be expendable. I hope that doesn’t happen. But unfortunately, it’s not impossible either.

–Lauren–

I’ve written in the past about inaccuracies in Google’s Search AI Overviews (let’s call them AIOs) that so often seem to provide specific answers to search queries, usually in rather authoritative wording. And there have been many concerns about how accurate or not these AIOs actually are. I’ve publicly noted a number of examples where AIOs were just flat out wrong even with simple math questions, which certainly doesn’t give one much confidence in more complex kinds of queries.

Now a firm called Oumi, led by a team of former-Google and former-Apple employees, has conducted a study that, combined with what Google does say about these issues, gives us considerable insight into some real statistics about Google AIOs. And those numbers are quite disturbing.

Of course Google has disclaimers all over the place saying that AI answers could be wrong and you should double-check them. But other studies have shown that hardly anybody bothers to do that. And why would you expect them to? What’s the point of being handed an “answer” if you can’t trust that’s it’s correct? And what does “correct” even really mean? Some answers from Google AIOs are fully correct, some are completely wrong, and some are only PARTLY correct — this last case can be particularly insidious. Even when Google AIOs provide links to what are supposedly the source sites related to an answer, often those sites had the information wrong themselves, and then Google unfortunately accepted it as correct and just stirred it into an AIO answer. Sometimes when you look you can’t even find where on those sites Google supposedly got that information at all — it just doesn’t seem to be there.

So what’s the scope of this problem as determined by this new study? Keep in mind this IS a tough nut to crack, for a bunch of reasons, one being Google AIOs are not particularly consistent. In fact, you can ask the same question twice just seconds apart, and, as you may have noticed you may get entirely different and even completely contradictory answers.

Another foundational point is that percentages alone don’t tell the whole story. The massive scale of Google — they have BILLIONS of users across their ecosystem — is absolutely crucial to consider. This applies whether we’re talking about their continuing account recovery policy failures when it comes to innocent users losing access to their accounts, or when it comes to this question of AIOs. The point is, at Google scale even relatively small percentages will represent vast numbers of actual human beings.

Summarizing the numbers it shakes out like this. There are a lot of factors involved but let’s be generous and say that AIOs are inaccurate overall about 9% of the time. There are significantly larger inaccuracy numbers when you look at specific related Google Gemini AI statistics and whether particular answers seem to be ungrounded as relates to referenced sites or inaccuracies at those sites.

Now let’s cut to the chase. At Google scale of around 5 TRILLION search queries a year — that’s trillions with a “T” — the study says that we’re dealing with on the order of tens of millions of wrong Google AIO answers EVERY HOUR, hundreds of thousands PER MINUTE. To me, especially when you consider the impracticality of really double-checking such answers, this is a terrible situation.

Google has become a misinformation machine on a colossal scale, virtually a mirror image of the trust we used to be able to put in their traditional search engine results without AIOs. Because it’s NOT the CORRECT answers we have to worry about, it’s the damage potentially done by wrong or partly wrong answers that people nonetheless understandably accept and act upon as if those answers were correct.

SCALE MATTERS, and enormous scale like Google’s matters enormously. But it’s the human beings around the world being impacted by that scale who Google should care about. The situation is made even worse since Google really won’t take responsibility for inaccuracies in their AIOs impacting those persons. Google is making money hand over fist, but when it comes to really caring about their users, they’re failing more than ever.

–Lauren–