AI 生成コンテンツ増加を受け、Pangram が検出技術に 900 万ドル調達

New York-based AI detection startup Pangram is on a mission to combat the AI slop infestation spreading across the internet, and it just raised $9 million on a bet that demand for tools that distinguish human-generated content from AI-generated text will only grow.

Pangram’s fundraise — led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza — comes as the startup also launches its next-generation AI text detection model, Pangram 4, and an AI image detection model, Pangram Image.

Pangram says the new text detection model is over 99% accurate at finding AI-assisted writing and mixed human-AI content, plus it can more easily detect AI humanizer programs. The AI image detector is only available via research preview for now; Pangram plans to release it more widely in the coming weeks.

Stanford AI and machine learning grads Max Spero and Bradley Emi launched Pangram about two years ago, after the launch of ChatGPT opened the floodgates for an internet full of bots, AI-generated SEO slop content, and what Spero calls “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.”

“I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?”

Pangram’s AI detection system is essentially a large machine learning model that was trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM.

“Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said, adding that the AI detector isn’t relying on copy-paste metadata or hidden watermarks.

For Pangram, AI detection isn’t just about whether or not a piece of text was written entirely by AI. It’s also about distinguishing between levels of AI assistance — like in the case of someone who writes something themselves, but then asks AI to edit or clean it up. Spero believes AI assistance can be acceptable, just so long as the writer discloses their use of AI.

Pangram’s emergence comes at a time when AI usage is becoming more commonplace. In some cases, like the Canadian politician who read an AI prompt aloud in a speech to lawmakers, the mistakes result in ridicule. In other cases, as with certain lawyers making their case using fake citations created by ChatGPT, the consequences could be sanctions and fines.

That backlash isn’t just costing individuals embarrassment or sanctions — it’s starting to show up in institutional rules, too.

The open-access archive arXiv introduced a new enforcement policy this year, stating that submissions containing evidence that authors failed to review LLM output (like hallucinated references or meta comments such as, “Would you like me to make any changes?”) can trigger a one-year submission ban.

Pangram isn’t the only one betting that AI detection will become more sought after. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are are chasing the same demand, each building its own detector.

Pangram’s technology, while not perfect, could help fuel the resistance to accepting the AI-generated content flooding the internet, the courtroom, and academic papers.

Users can access Pangram via a $20-per-month subscription on the web or download the Chrome extension, which automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen.

Pangram also offers its technology via API. Notably, Substack recently integrated Pangram’stechnology into its platform to show readers which of their favorite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero.

Does Pangram work?

Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model, so I decided to put it to the test. The text detection model was very impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content.

I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate, but the model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score.

Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text.

My limited testing of Pangram’s new image detection model turned out to be equally impressive.

Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo.

In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content.

Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop.

“The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”

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