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- 🤖 Fake AI, Real Money
Who’s really building AI… and who’s just wrapping an API? A leaked OpenAI customer list just exposed who’s selling “fake AI” to millions - and they’re not small names. NVIDIA projects $4 trillion pouring into AI infra by 2030, while Stanford quietly dropped a paper that could end fine-tuning as we know it.
What are on FIRE 🔥
LEARNING PARTNER AIRCAMPUS
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40+ apps humming in the background. ⚙️🤖
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It’s a Masterclass showing you how to architect AI agents that actually run your work -across 40+ tools - so you can focus on the parts only you can do.
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AI INSIGHTS
The State of AI 2025 report is out, breaking down everything from NVIDIA's circular revenue deals to why the top AI models are now strategically faking their safety alignment.
If you follow AI, this is the annual benchmark, co-reviewed by top researchers across industry and academia. Key takeaways:
1. OpenAI Leads, But China Is Catching Up → OpenAI is still number one, but China’s DeepSeek, Qwen, and Kimi are now close behind. Meta has slowed down, while China has become a serious competitor.
2. The Year of Reasoning → This year was about smarter reasoning. AI systems can now plan, reflect, and fix their own mistakes using feedback loops and better reward systems. They think.
3. AI as a Scientific Collaborator → AI is now working with scientists. DeepMind’s Co-Scientist and Stanford’s Virtual Lab can design and test experiments automatically.
4. From Chain-of-Thought to Chain-of-Action → AI moved from “thinking” to doing. Tools like Gemini Robotics 1.5 plan every step before they move.
5. Commercial Adoption Exploded → Businesses are now all in. 44% of U.S. companies pay for AI tools. Average deal: $530,000. AI-first startups grow 1.5× faster. 95% of workers use AI.
Why it matters: AI has officially gone mainstream. The new challenge now is making sure these AIs stay honest when the reward is attention, money, or control. Can we control what it’s optimizing for?
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TODAY IN AI
AI HIGHLIGHTS
💥 OpenAI’s “fake AI” moment. A leaked customer list shows big names wrapping the API and selling it as their own. Duolingo, Salesforce, HubSpot are on it. See the leaked list.
🧑🍳 Qwen drops a packed set of multimodal notebooks. From OCR and 3D grounding to mobile agents and video understanding. Start with the Cookbooks.
🏢 Google launches a company-grade AI layer for real work. One assistant across Workspace, plus secure agents and governance. Explore Gemini Enterprise.
📱 Apps aren’t dead, but the icon may be. ChatGPT’s app system is growing, yet Apple’s Siri reboot could still win on iPhone. Read the full analysis.
🧨 PR meets policy at OpenAI. Chris Lehane faces hard questions on Sora, subpoenas, and energy. Read the full story.
💰 AI Daily Fundraising: Feedzai raised $75M at a $2B+ valuation to boost its AI platform fighting financial crime. Backed by Lince Capital and Iberis Capital, Feedzai protects $70B+ in annual payments and has prevented $2B in losses.
AI SOURCES FROM AI FIRE
NEW EMPOWERED AI TOOLS
🎯 Intryc scores support tickets with 90% precision and AI simulations that cut onboarding time in half.
📞 Lyra records, transcribes & turns every meeting into action items - no notetaker needed.
🗣️ Layercode CLI builds voice AI agents from your terminal in one command with global deployment.
💰 blai is your AI crypto advisor, trading live from prompts and building portfolios for you.
AI QUICK HITS
🌎 Nvidia predicts up to $4 trillion will be spent on AI infrastructure by 2030 - with Microsoft, Google, and Oracle racing to build next-gen data centers.
🏗️ OpenAI and Sur Energy are planning a $25B Stargate Argentina data center - Latin America’s biggest AI project yet.
🚫 China launches a customs crackdown on Nvidia’s AI chips - enforcing strict bans on U.S. processors to push local alternatives.
🚀 OpenAI’s dominance is now unmatched in Silicon Valley, rolling out Sora 2, Codex, and a Jony Ive-led AI hardware project after a $6.4B talent hire.
📉 Top investors warn of an AI bubble - with “circular financing” between Nvidia, AMD, and OpenAI drawing comparisons to the dot-com crash.
AI CHART
A new Stanford paper just dropped and it might quietly bury the entire fine-tuning industry. It’s called Agentic Context Engineering (ACE).
Instead of tweaking model weights, ACE tweaks the prompt itself. Over time. Like a living brain. Here’s how it works:
Generator → runs the task
Reflector → critiques performance
Curator → updates the context with what worked
Each loop adds small, focused changes, called delta updates, that never overwrite what came before. ACE beat everything it went up against, even agents powered by GPT-4.1:
+10.6% over GPT-4 agents on AppWorld. +8.6% on financial reasoning (FiNER). 86.9% lower latency than fine-tuned systems. 80% cheaper
And it matched the performance of IBM’s GPT-4.1 agent… using a smaller open-source model. It’s like watching your model keep a diary of what works and what doesn’t.
TL;DR: ACE is cheaper, more transparent, and scales faster. You got it!?
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The AI Fire Team
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