How many Google AI researchers does it take to screw in a lightbulb?A recent paper revealing the technical core behind Google's Gemini AI assistant answers humorously with 3, 295 authors listed. Machine learning researcher David Ha ("hardmaru") highlighted a hidden message within the first 43 authors’ initials: "GEMINI MODELS CAN THINK AND GET BACK TO YOU IN A FLASH. " This Easter egg relates to the paper—titled "Gemini 2. 5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities"—which details the Gemini 2. 5 Pro and Flash AI models powering Google's chatbot. These models simulate reasoning by "thinking out loud" before responding, hence “think” and “flash” in the secret code. The enormous authorship list reflects the scale of modern AI development. While 3, 295 authors mark a massive collaboration within Google, it is not record-breaking. Guinness World Records cites a 2021 COVIDSurg paper with 15, 025 authors and a 2015 CERN Large Hadron Collider physics paper with 5, 154 authors—examples from fields where large-scale teamwork is common. CERN’s work, for instance, involved thousands of scientists and engineers across multiple institutions, a necessity mirrored in complex AI projects. Developing Gemini models requires diverse expertise—from machine learning researchers and software engineers to hardware specialists, ethicists, product managers, and domain experts—reflecting multidisciplinary collaboration.

The jump from 1, 350 authors on Google’s initial 2023 Gemini paper to 3, 295 for Gemini 2. 5 represents a 144% increase in less than two years, underlining the growing complexity of AI research. This raises questions about modern AI research as a team effort and the definition of authorship. Google’s inclusive authorship contrasts with other AI labs like OpenAI, whose o1 and GPT-4o System Cards list far fewer authors (260 and 417 respectively), possibly due to company size or credit policies. Such extensive author lists can blur distinctions between core contributors and peripheral participants, complicating assessment of individual impact and potentially inflating citation metrics artificially. Critics of large collaborations argue many listed authors may never have read or contributed substantially to the paper. While credit may be due, such numbers become unwieldy. If authorship on Google AI papers continues to grow by 144% every two years, by 2040 a single paper might list over 2. 65 million authors—requiring AI just to parse the author list itself. The Gemini paper highlights both the colossal cooperative effort behind cutting-edge AI and the evolving challenges of recognizing contributions in ultra-large teams.