👀 Nvidia, OpenAI, Gemini

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Is Nvidia actually replaceable? Is Gemini really a threat to OpenAI? And why are smaller AI models suddenly winning?

What’s on FIRE 🔥

• 🔥 Why Nvidia’s Moat Still Holds (And Gemini Won’t Kill OpenAI)

• 📚 AI Sources From AI Fire

• ⭐ Today’s AI Highlights

• 🧾 Hot Papers of the Week

• 🏅 AI Tools

• 📊 AI Breakthrough

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AI INSIGHTS

🔥 Why Nvidia’s Moat Still Holds (And Gemini Won’t Kill OpenAI)

Engagement, not just MAUs: Why ‘user minutes’ changes the economics of AI + ads

Two popular takes are everywhere:

• “TPUs will break Nvidia.”

• “Google’s Gemini will beat OpenAI.”

This analysis says both are overstated.

Nvidia’s moat isn’t chips. It’s AI factories.
Frontier AI is bandwidth-heavy. Scaling cleanly matters more than raw speed. Nvidia wins on full systems: GPUs, interconnects, software, and scale.

TPUs are useful, but limited.
They work well for Search and bounded tasks. For massive, distributed training, GPUs still dominate.

Why TPUs look hot right now
Supply is tight. CoWoS packaging is the real bottleneck. Nvidia locked up capacity, which drives volume and lower costs over time.

Gemini’s real problem
AI-style search costs far more to run. That clashes with Google’s ad-based economics.

OpenAI’s edge
Paid users, strong APIs, and growing enterprise pull. As models converge, platforms and workflows matter more than “best model.”

Why it matters: This is about economics and execution, not headlines. For now, Nvidia and OpenAI are still ahead.

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AI SOURCES FROM AI FIRE

1. Stop getting AI Slop: 10 advanced Claude rules to unlock 10x better output. Transform Claude from a basic tool into your powerful, strategic partner

2. Google Mixboard: Turn ideas into mockups + brand boards in 30 mins. How to brainstorm, mock up products, pitch a whole brand without opening 5 tabs

3. How to repackage any AI into a repeatable money machine (You can copy this). Most fail by just using chatbots. Real winners repackage them. See how to turn boring problems into cash with this proven, simple business model

4. AI is changing how companies actually win: 5 shifts you can’t ignore. If you wanna success, here’re 5 shifts I’m seeing and what to automate first

TODAY IN AI

AI HIGHLIGHTS

🍬 OpenAI + Anthropic are doing a holiday boost: Codex users get 2× usage limits until Jan 1, 2026, and Claude Pro/Max get 2× from Dec 25–31. If you hit caps while shipping, this matters.

☢️ AI safety expert at UC Berkeley, Stuart Russell, warns that the AGI arms race won’t slow down without government action – and real regulation may only come after a Chernobyl-scale disaster.

🔮 A 2026 tech roundup says AI is moving beyond chatbots into world models, system-level assistants, smarter Siri, autonomous cars, and deeper use in healthcare and security. This frames the key 2026 predictions.

💍 2026 could be a breakout year for AI hardware. One standout: Sandbar’s Stream, a note-taking smart ring that records whispers and acts on commands. The most intriguing hook is the AI ring itself.

📌 A year-ender says 2025 was the inflection point when AI became embedded everywhere – from reasoning models to agents. The geopolitical shocker was China’s open-source rise, led by DeepSeek R1.

💰 AI Deals & Fundraising: Alphabet is acquiring Intersect Power for $4.75B to secure energy for AI data centers. The deal boosts Google’s AI capacity as it competes with OpenAI, xAI, and Meta.

HOT PAPERS OF THE WEEK

1. DataFlow: Unified Data Prep for LLMs
   DataFlow turns messy data work into clean pipelines. A PyTorch-style API + agents beats human-curated data and lifts math, code, and Text-to-SQL results with far less data. (Peking University • Shanghai AI Lab)

2. Probing Scientific General Intelligence (SGI-Bench)
   Researchers define Scientific General Intelligence and test it end-to-end. Results show LLMs know science – but still struggle with deep research, experiments, and reasoning loops. (Shanghai AI Lab)

3. Step-DeepResearch Technical Report
   A 32B agent reaches expert-level deep research at low cost. It rivals OpenAI and Gemini on real-world benchmarks and scores 61.4% on Scale AI rubrics. (Step-DeepResearch Team)

4. TurboDiffusion: 100–200× Faster Video Generation
   TurboDiffusion accelerates video diffusion by up to 200× on a single GPU using attention tricks, distillation, and 8-bit quantization – without losing quality. (Tsinghua • UC Berkeley)

5. PhysBrain: From Egocentric Video to Robot Intelligence
   PhysBrain trains robots using human first-person videos, boosting planning and control. It hits 53.9% success and transfers efficiently to embodied tasks. (HKUST(GZ) • ZGC AI Institute)

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AI BREAKTHROUGH

🧪 A Tiny AI Model Just Beat a Giant

A 2.6B-parameter model just outperformed DeepSeek R1-0528, which is 263× larger.

That model is LFM2-2.6B-Exp from Liquid AI. It’s trained with pure reinforcement learning, not just labeled data.

Why it matters:

• Beats much larger models on instruction-following benchmarks

• Runs with far less compute

• Easy to fine-tune for specific tasks

Best at:

• Agents

• RAG

• Data extraction

• Creative writing

Not ideal for

• Deep factual knowledge

• Heavy coding

Big idea: Small, well-trained models are catching up fast. Bigger models are no longer the default answer.

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The AI Fire Team