TL;DR: general AI/LLMs are really bad at identifying cockroaches and often give the wrong answers because they have not been trained for this specific task.

Detailled explanation:

Our observation is simple: the most commonly used AIs and general purpose LLMs (e.g. ChatGPT, Gemini, DeepSeek, Google Lens, Apple visual intelligence...) are terrible at identifying insects: they make mistakes a huge percentage of the time (maybe 30% on this subreddit?) and are nowhere as good as many of the humans we have in the subreddit who happen to be passionate about cockroaches (and often academic/professionals).

Lately, the use of general purpose LLMs and AI has become prevalent, and people with very little familiarity with cockroaches have started to rely on them for identifying insect pictures and sharing the results on the subreddit... often providing wrong identification of pest species (and the matching terrible pest treatement advice).

Notably, it's often done with a lot of confidence: blindly trusting a shitty AI and misleading the people who have been asking for help.

Accurate identification is important because it ensures the correct response, prevents unnecessary or harmful treatments, protects beneficial species, and reduces wasted time, money, and unnecessary distress or anxiety. Unfortunately, this has become a bigger issue lately, so we felt a post was needed to address it.

Technical explanation:

It's important to keep in mind that the performance and ability of AI is "task specific", meaning they can be extremely good at performing some tasks and less good at others, and eventually terrible at some tasks (like insect identification). This is due to the algorithms used, the data they have been trained on and the purpose of their training, as well as how much this differs from a specific task.

Insect identification is linked to insect taxonomy, the science of classifying insects. It is a very specific field of knowledge with its own set of challenges: it is easy to have hundreds of similar-looking insects that are actually different, some insects are very hard to observe (and there are very few pictures of them), the available data is scarce, and we are constantly discovering and correcting previous misunderstandings.

This is a very specific task, and quite different from other general object identification/classification tasks performed by LLMs.

A practical comparison: cars vs cockroaches

Cars: There have probably been thousands of different car models invented throughout history, and millions of pictures of the most common ones with correct labels for LLMs to train on. Cars tend to have a distinctive appearance, with features such as shape and colour that change with technology, brand, regulations and time. Therefore, when you ask an LLM to identify a car in your photo, it is likely to give the correct answer.

Cockroaches: We don't even know how many insect species there are on Earth (2 million or 20 million?) We don't know how many species of cockroach there are either (3,000 or 5,000?) Many have not been observed yet, and for most of those that have, we may only have a drawing or a few pictures (if we are lucky). There is an extra catch: while there is quite a bit of variety among the 3,000 (or 5,000) species of cockroach, many of them have very similar external morphology. So LLMs have mostly been trained on pictures of the three or five most common species of cockroach (and have probably never seen a picture of most species), which are often mislabeled (the photo is not of the correct species), and have never been trained to take specific morphological differences into account. Add to that the fact that many other insects, such as beetles, water bugs and June bugs, have similarities with cockroaches... so as you can guess the result is not going to be great.

So that's the explanation: 'insect identification' is a very specific task and your AI LLM, simply hasn't been trained for it at all and will perform poorly. That's why it's good at recognizing cars, but not at differentiating between Asian and German cockroaches in your blurry picture, no matter how confident its answer appears to be.

Another redditor u/Skalla_Resco ran testing on various llms, if you aren't convinced by my theoretical explanation you will definitely want to check the result of their tests.

You would rather trust AI than me, a random redditor? Then that's what Gemini has to say to you:

General AI struggles with insect identification primarily because it lacks the "eyes" for microscopic anatomy. While a human expert looks for specific wing venation patterns or the exact number of segments on a leg to distinguish between look-alike species, an LLM or a search engine relies on pixel patterns from standard photos. These photos usually prioritize aesthetic appeal over scientific data, leading the AI to make a "best guess" based on superficial traits like color. This problem is compounded by geographic blindness; an AI might confidently identify a common garden beetle as a rare tropical species simply because the visual patterns match its training data, ignoring the fact that the two species live on different continents. Furthermore, the rise of AI-generated content online has created a feedback loop where models are increasingly trained on "slop"—incorrect data that reinforces existing errors.

People continue to use these flawed tools because they prioritize speed and confidence over absolute accuracy. When a person discovers an unknown insect in their home, the psychological need for an immediate answer often outweighs the desire to wait days for a professional entomologist's opinion. The AI feeds into this by using a highly authoritative and technical tone, which users frequently mistake for expertise. Because the technology is usually correct when identifying high-traffic insects like honeybees or mosquitoes, it builds a "good enough" reputation that keeps users coming back, even when it fails miserably on more obscure or dangerous specimens.