🕵️ Google’s AI Researchers = 7x OpenAI’s

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Google’s Gemini 2.5 paper doesn’t just have 3,295 authors, it hides a secret message. Plus: agent upgrades, Claude drama, and the most viral AI art online right now.

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What are on FIRE 🔥

• 📜 3,295 Authors & a Hidden Message? Google’s Gemini Paper is a Whole Vibe

• 🌟 AI Highlights

• 📚 AI Sources From AI Fire

• 🏅 AI Tools

• ⚡ 5 AI Quick Hits

• 📊 AI Chart

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AI INSIGHTS

📜 3,295 Authors & a Hidden Message? Google’s Gemini Paper is a Whole Vibe

How many AI researchers does it take to write a paper? According to Google: 3,295, plus one secret Easter egg.

This week, researchers discovered that Google’s Gemini 2.5 paper also hid a surprise. If you take the first initials of the paper’s first 43 authors, there’s bigger message: AI development has become a massive team sport, model papers starting to look more like phone books.

By comparison:

• Gemini 1 (2023) had “just” 1,350 authors.

• GPT-4o System Card: 417 authors.

• o1 System Card: 260 authors.

Google’s author list grew massively in under two years (from 1,350 to 3,295). It seems to be especially inclusive in crediting everyone involved. It’s different from how OpenAI or Anthropic do it, where credits are still large, but more selective.

One sarcastic takeaway: If authors keep increasing 144% every two years… by 2040, Google AI papers might need 2.6 million names. Hope you like scrolling.

And honestly? If the models can think and get back to us in a flash, we might soon need them to summarize their own authorship lists too.

Why It Matters: AI model papers are becoming multi-disciplinary artifacts, like scientific missions or particle physics collaborations. With thousands of names, it’s getting harder to tell who did what. Should we coordinate at Google-scale to move it forward?

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TODAY IN AI

AI HIGHLIGHTS

🎥 Even Veo 3 only gives you 8-second videos, but this new real-time model literally lets you direct a full minute of AI video & adjust prompts mid-generation. Try it here.

📹 Model mashup: Flux Pro + Seedance = This custom video model from an X user with the most hyper-realistic results I’ve seen. Try it here, or see more examples here.

🤖 OpenAI’s most powerful feature yet can control an entire computer & do tasks for you. Agent mode = ChatGPT + Virtual computer. It can now “think” and “act” for you.

🧠 Mistral just upgrades Le Chat assistant with a “deep research” mode, native multilingual reasoning, image editing. It’s an open alternative to Microsoft Copilot. See it here.

🌀 Anthropic just hired back 2 key leaders behind Claude Code from Cursor’s parent company, just weeks after the exit. This is Anthropic ‘Uno Reverses’ talent poach.

💰 AI Daily Fundraising: Perplexity AI just raised $100M, boosting its valuation to $18B – up from $14B earlier this year. Founded in 2022, it’s now one of Google’s top AI search rivals.

AI SOURCES FROM AI FIRE

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AI QUICK HITS

1. 🖼️ Viral AI art by Perplexity’s ambassador, what makes people stop scrolling

2. 🤐 Anthropic quietly tightens Claude limits, $200 Max users left in the dark

3. 🎧 Adobe’s new AI tool just turns your silly noises into realistic audio effects

4. 🛒 OpenAI is working on a native checkout system to complete purchases

5. 🖥️ Microsoft Copilot can now “see” whatever on your screen in real time

AI CHART

⚗️ A Self-Driving AI Lab Just Made Material Discovery 10x Faster

Move over, slow science. Researchers at North Carolina State University just built a fully autonomous AI-powered chemistry lab, it runs continuously, collects 10x more data, and finds promising materials for clean energy, electronics, and sustainable chemicals in days instead of years.

Instead of running one chemical reaction, recording the result, then resetting → this system captures a new data point every 0.5 seconds, adjusts experiments mid-run, and never stops testing.

And because it’s efficient, it uses way fewer chemicals, cuts lab waste, and is way more sustainable. Here’s what it’s doing right now:

• Streaming chemical reactions like Netflix — no pauses.

• Running experiments that normally take weeks… in hours.

• Zeroing in on the best new materials before you finish lunch.

Now, there are already a few systems out there that help with scientific research, but none that fully automate the loop of design → test → learn → repeat like this one. The closest comparison might be companies like DeepMind’s AlphaFold or NVIDIA’s drug discovery pipelines, but even those don’t operate in real-time physical labs.

And now that this kind of lab is possible, you’ve gotta wonder: Where’s the “OpenAI of chemistry”? Where’s the “Claude for clean energy materials”?

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