Recently, as I was scrolling through my phone, I realised I was suffering from a new kind of fatigue: slop-induced burnout. Almost all the posts I was reading on social media were riddled with bullet points, emojis, and em dashes. They clearly looked like the work of a chatbot.
This month, my suspicions were confirmed by a web tool that is getting buzz: Pangram, which uses artificial intelligence (AI) to detect when words were generated with chatbots. Users can simply paste in a block of text for Pangram to scan, and the tool calculates a percentage indicating how much of the text was churned out by a bot or written by a person.
I tested Pangram, which costs US$20 (RM81.68) a month, for about a week and tried all sorts of experiments to challenge the system. I fed it personal essays alongside text generated with chatbots instructed to imitate my writing. I also uploaded social media posts from LinkedIn and Reddit that I suspected were AI-generated. In dozens of tests, the detector never failed at distinguishing slop from human writing.
Though Pangram is one of many AI detectors that have hit the market in recent years, the product stands out because of its accuracy. Pangram says its technology can accurately identify 9,999 out of 10,000 times whether text was AI-generated; an independent study by the University of Chicago also found that the tool was near-perfect with text. In contrast, past AI text scanners made glaring errors, including the misidentification of writing by famous authors like Charles Dickens.
At the rate that slop is proliferating on the web – some studies say 50% of online articles are now artificially generated – AI detectors could become the next staple app, akin to antivirus software, that people will need to shield themselves from phoney, low-quality information online.
Technology for scanning AI content, however, still has a long way to go. Like other AI detectors, Pangram isn’t very good at its other job: identifying AI-generated images, which are a more potent medium for spreading misinformation online. (More on this later.)
Here’s what you need to know.
How AI detection works
In the same way that AI chatbots excel at analysing patterns in our creations to replicate writing and imagery, detectors like Pangram analyse data collected from AI models to decipher the characteristics of AI-generated writing and pictures.
For detecting AI writing, the methodology is not as simple as looking for em dashes and bullet points. Every AI chatbot, such as Claude, ChatGPT and Gemini, follows a “decision tree,” where each word chosen is a decision that leads to another, said Max Spero, a founder of Pangram. Accurate detection of AI writing is rooted in understanding how each AI model’s decision tree works, which involves gathering an enormous amount of data on each model, he added.
“We’re reverse engineering their stylistic fingerprint,” Spero said.
With AI-generated images, there are telltale signs, like textures inside pixels that look rough when they should be smooth, oversaturated colours or inconsistencies in lighting.
Some companies behind AI picture generators, including OpenAI, Anthropic and Google, also embed invisible fingerprints, known as watermarks, in an image’s metadata to reveal to detectors that something is generated with AI.
Watermarks are also coming soon to AI-produced writing. Anthropic announced this week that it planned to embed an invisible watermark in writing produced by its Claude chatbot to help people detect its AI-generated text. The move was a response to European Union regulations requiring transparency of AI content; other AI companies are expected to follow suit.
Testing words
I ran about 50 tests on Pangram’s word detector. I tried pasting passages of my personal writing with some AI-generated sentences sandwiched in between. Pangram correctly detected that the prose was mostly human-generated and highlighted the few sentences that a chatbot had produced.
I also tried the opposite: sprinkling some of my writing within paragraphs of AI slop. Again, Pangram correctly called out my writing as human and said the rest of it was AI.
I tried uploading publicly available passages from Dickens. Pangram concluded they were 100% human-written.
I scanned a handful of LinkedIn posts that appeared to have been written by a machine. Pangram revealed that one tech worker had written the introductory sentence of his post but used AI to generate the rest. (I messaged the worker, who sheepishly confirmed this.) The experience felt empowering – for a moment I felt like Nada in the movie They Live when he donned sunglasses to detect aliens.
Picture imperfect
I took a different approach to testing AI photo detection: I uploaded 21 AI-generated images that had been widely shared online and debunked by news outlets into Pangram and Hive Detect, a similar AI image detector. My sample size was small, but Pangram and Hive Detect failed so quickly that I didn’t see a point in continuing. (My colleague Stuart Thompson did a more comprehensive test of AI image scanners this year.)
Hive Detect incorrectly identified nine of the AI-generated images as real, including a recent deepfake that made actress Zendaya appear to be pregnant, a picture of US Senator Mitch McConnell on a hospital bed and a photo depicting a street sign in San Francisco that suggested it was OK to steal goods under US$950 (RM3,880) from stores.
Pangram incorrectly identified three AI images, including the one of McConnell and the phoney sign in San Francisco. But the tool also declined to scan four images that looked violent or were too low quality to make a guess.
Both detectors correctly flagged some famous fakes, including a photo portraying the wedding of Zendaya and her Spider-Man co-star Tom Holland, a photo of President Donald Trump holding a girl during his visit to China and a photo of the Clintons partying with Jeffrey Epstein.
Hive said that in some of my tests, such as the photo of McConnell, I may have scanned a copy of the image shared on social media lacking details of the original, which could lead to an incorrect result. My concern with this rationale is that by the time most people see AI slop online, the image is either a screenshot of the original or has been automatically shrunk down by the social media platform. If that’s all it takes to thwart AI detectors, the technology is pretty unhelpful.
Spero of Pangram said he was surprised that his tool had failed to flag the image of McConnell but added that AI image detection was a new, unfinished feature for the product. In his company’s tests of 43 AI images, he said, Pangram correctly identified 41.
The bottom line
Despite Pangram’s issues with spotting AI imagery, its proficiency in detecting bot-produced words will make it very useful for sniffing out annoyances like AI-written email scams, phoney online reviews and uninteresting LinkedIn posts.
AI-produced photos, which spread rapidly online, remain a much greater problem. Hany Farid, a Dartmouth professor and a founder of GetReal Security, a company that verifies the authenticity of digital content, said he had run his own quick experiment with Pangram’s image scanner. He uploaded five AI-generated wartime photos, and Pangram flagged three.
Farid said distinguishing fake and real photos was extremely hard because an image could be distorted and manipulated in many different ways. In his research, visual AI detectors generally were still too flawed.
For now, he suggested that people rely on trusted media outlets for real information.
“Stop getting your news from social media,” Farid said. “An assumption that most of what you are seeing is fake is probably pretty good right now.” – ©2026 The New York Times Company
This article originally appeared in The New York Times.
