{{first_name | Leader}}, welcome back.
If you missed updates from last week: GPT 5.1 came out, Google added model encryption, the EU relaxed some AI rules, energy limits slowed AI plans, and Google’s new chips may cut costs.
Today’s updates:
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Google releases new Gemini model.
Samsung plans big spending on AI.
Hugging Face says LLM hype may burst.
Tools, resources, and a prompt to estimate how long an initiative or project will take to show measurable business impact. ⬇️
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Google has officially launched Gemini 3, and it is Google’s most intelligent model so far, built for deeper reasoning, stronger multimodality, and real agentic workflows.
What stands out
It can work across text, images, video, audio, and code in one flow, showing clear gains in accuracy and reasoning.
Gemini 3 Pro is live in Gemini Enterprise and Vertex AI, so teams can start using the model inside existing stacks.
The new coding abilities let you generate front-end prototypes, migrate legacy code, and run tests with fewer steps.
The Gemini app now lets you choose Thinking mode, use multimodal input, and explore a cleaner interface.
Things to keep in mind
Many features are still in preview, especially Deep Think and some agentic capabilities.
I’m looking forward to seeing how enterprises use it for real workflows, especially now that it is fully available in Gemini Enterprise and Vertex AI.
In other news
Samsung announced a $310B investment plan, targeting AI technology infrastructure and expansion over the next five years. The initiative follows the government’s recent pledge to triple spending on AI domestically.
Hugging Face CEO Clem Delangue warns the hype is around LLMs, not AI overall, and predicts an LLM “bubble” may burst soon. He says AI’s future is safe, with growth shifting to smaller, specialized models instead of just big LLMs.
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One Chart that Matters
By 2027, what share of the typical AI stack is expected to be custom-built vs. vendor-supplied?
💰 Funding
Lambda raised over $1.5B to expand its GPU cloud and accelerate large-scale AI training.
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💼 Roles in AI
🐦 Cursor’s Tech Stack
Time-to-Impact Calculator
When to use this?
When you want to quickly estimate how long an initiative or project will take to show measurable business impact, before committing budget or resources.
You are a business impact analyst.
Evaluate the initiative I’ll describe below and estimate:
Expected time to first measurable impact (in weeks or months)
Key factors that speed up or delay impact
Early indicators that show it’s working
1 recommendation to accelerate results
Output in a short table:
| Metric | Estimate / Insight |
Keep total output under 150 words.
Initiative: [describe project or idea briefly]Correct Input Style:
“Initiative: launch an AI-powered customer onboarding chatbot to reduce drop-offs during setup.
Goal: cut onboarding time by 25%.
Budget: $250K. Target launch: next quarter.”
P.S. Get more such prompts in the Prompting Playbook (free for you)
Q. What is the top reason AI pilots typically stall before scaling?
Answer: The #1 reason AI pilots fail to scale is primarily due to lack of measurable business value and ROI alignment.
Stay curious, {{first_name | leaders}}
PS. If you missed yesterday’s issue, you can find it here.