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- 🧠 Claude Code Rewards AI Experts
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The next AI winners may not be the fastest coders or the biggest chip buyers. Anthropic and Tensordyne both point to the same shift: AI is starting to reward better judgment and cheaper execution.
What's on FIRE 🔥
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AI INSIGHTS
Anthropic analyzed around 400,000 Claude Code sessions and found one clear trend:
AI coding is becoming less about writing code, and more about knowing what to build.
In most sessions:
Humans make about 70% of planning decisions
Claude handles around 80% of execution decisions
Users decide what they want
Claude figures out how to build it
The biggest winner is not always the best coder. It is the person with the strongest domain expertise.
Expert users get far more from Claude:
Novices trigger about 5 Claude actions per prompt
Experts trigger about 12 Claude actions per prompt
Claude Code is also moving beyond debugging. From October 2025 to April 2026, debugging sessions dropped from 33% to 19%, while use cases like deployment, data analysis, and writing documents grew.
The big lesson: Judgment is becoming the new coding skill. Claude Code is not replacing expertise yet. It is rewarding it.
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AI SOURCES FROM AI FIRE
1. Free Guide: Gemini Plus vs ChatGPT Go: Which $5–$8 Plan Actually Fits You After 30 Days. Both cost under $10 a month. We compare real pricing, usage limits, and feature breakdown to help you pick the right one for your work.
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3. Google Antigravity 2.0 vs Claude Code vs Codex. Google’s new AI coding tool is free, agent-first, and built for faster projects. See how it compares with Claude Code and Codex, then use it to build a premium D2C website from scratch.
FIRE RECAP: BIGGEST AI NEWS THIS WEEK
🧠 Google had a rough week in the AI talent war. John Jumper, the Nobel-winning AlphaFold co-creator, is leaving for Anthropic, while Noam Shazeer, a Transformer-era researcher and Gemini leader, is joining OpenAI.
🩺 OpenAI says GPT-5.5 Instant is now much stronger for health questions. More than 230M people ask ChatGPT health and wellness questions every week, and flagged health factuality issues dropped 71% in two months.
🧪 OpenAI and Molecule.one connected GPT-5.4 to Maria, an agentic chemistry AI linked to a real lab. It helped improve a tough chemistry reaction, with better yields across 88% of boronic acids and 83% of sulfonamides tested.
🛡️ Google DeepMind released its new AI Control Roadmap for safer agents. The main idea: as AI agents get more powerful, companies need system-level controls, not just better model alignment.
🏢 OpenAI launched its new Partner Network to help companies adopt AI faster. It is investing $150M and aims to train 300K certified consultants by the end of 2026.
TODAY IN AI
AI HIGHLIGHTS
🏛️ Trump now says Anthropic is no longer a national security threat, just two days after meeting CEO Dario Amodei at the G7 summit. But the Mythos 5 and Fable 5 export limits still remain.
🏫 Norway is putting a near-ban on generative AI in elementary schools. Kids aged 6 to 13 generally won’t use AI in class, while older students can use it only under teacher supervision.
📱 Apple is bringing more practical AI features to iOS 27, beyond Siri. Your iPhone can split bills from receipts, update weak passwords, suggest actions in Messages, organize Safari tabs, and build Shortcuts from plain text.
🔐 Anthropic’s Mythos fight is starting to look like the old PGP and spyware export-control battles. The big question now: can governments really control frontier AI the way they tried to control encryption?
🛍️ Amazon, H&M, Zara, Ikea and other retailers want AI-generated ads exempt from EU AI labels. Their argument: normal product visuals shouldn’t be treated like deepfakes if they are not meant to mislead shoppers.
💰 AI Daily Fundraising: General Intuition is reportedly raising $300 million at a $2 billion valuation to train AI agents using Medal’s dataset of 2 billion gameplay videos from 10 million monthly users.
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AI BREAKTHROUGH
Tensordyne just unveiled a new AI-inference rack called Napier, it claims their new system delivers 13x the rack throughput of NVIDIA’s NVL72 GB300 when running DeepSeek-R1.
Napier operates in log space, where heavy multiplication operations are converted into simple addition. Addition is significantly cheaper to build, switch, cool, and repeat billions of times per token.
Napier frees up chip area for more SRAM. This architectural shift results in less power consumed per token and allows far more inference capacity to be packed into a single rack.
Tensordyne reports that their TDN Rack reaches 363,000 tokens per second on DeepSeek-R1. For context, they benchmark Nvidia’s NVL72 GB300 at 27,400 tokens per second under the same internal simulation conditions.
If Tensordyne has successfully made log math fast and accurate enough for real-world deployment, this changes the fundamental cost structure of model serving.
AI inference is no longer just a FLOPS race. It has evolved into a rack-level battle over power efficiency, memory locality, interconnect latency, and the sheer volume of paying tokens a single system can economically serve.
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