Why it takes 3,295 people to write one Google AI paper

How many Google AI researchers does it take to screw in a lightbulb? A recent research paper detailing the technical core behind Google’s Gemini AI assistant may suggest an answer, listing an eye-popping 3,295 authors.

It’s a number that recently caught the attention of machine learning researcher David Ha (known as “hardmaru” online), who revealed on X that the first 43 names also contain a hidden message. “There’s a secret code if you observe the authors’ first initials in the order of authorship,” Ha wrote, relaying the Easter egg: “GEMINI MODELS CAN THINK AND GET BACK TO YOU IN A FLASH.”

The paper, titled “Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities,” describes Google’s Gemini 2.5 Pro and Gemini 2.5 Flash AI models, which were released in March. These large language models, which power Google’s chatbot AI assistant, feature simulated reasoning capabilities that produce a string of “thinking out loud” text before generating responses in an attempt to help them solve more difficult problems. That explains “think” and “flash” in the hidden text.

But clever Easter egg aside, the sheer scale of authorship tells its own story about modern AI development. Just seeing the massive list made us wonder: Is 3,295 authors unprecedented? Why so many?

Not the biggest, but still massive

While 3,295 authors represents an enormous collaborative effort within Google, it doesn’t break the record for academic authorship. According to Guinness World Records, a 2021 paper by the COVIDSurg and GlobalSurg Collaboratives holds that distinction, with 15,025 authors from 116 countries. In physics, a 2015 paper from CERN’s Large Hadron Collider teams featured 5,154 authors across 33 pages—with 24 pages devoted solely to listing names and institutions.

The CERN paper provided the most precise estimate of the Higgs boson mass at the time and represented a collaboration between two massive detector teams. Similarly large author lists have become common in particle physics, where experiments require contributions from thousands of scientists, engineers, and support staff.

In the case of Gemini development at Google DeepMind, building a family of AI models requires experience spanning multiple disciplines. It involves not just machine learning researchers but also software engineers building infrastructure, hardware specialists optimizing for specific processors, ethicists evaluating safety implications, product managers coordinating efforts, and domain experts ensuring the models work across different applications and languages.

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