^{AI MARKET PULSE}
AI Job Losses Are Becoming Policy — Not Theory

AI-driven layoffs are moving from projections to execution. Major research firms warn that a meaningful share of U.S. work hours can already be automated, and executives privately admit they plan to run leaner companies as AI capability expands. High-cost roles, routine white-collar work, and positions without AI leverage are increasingly viewed as replaceable. This isn’t a distant scenario — companies are slowing hiring, automating tasks, and restructuring around software-first labor today.

So what does that mean for you?
Waiting is the risk. The same forces reducing headcount are creating opportunity for those who move early:

• Build AI automation or implementation services
• Create products or training around AI adoption
• Position yourself as adaptable, not overhead

Workforce disruption is happening quietly but quickly. Those who reskill now gain leverage as automation accelerates; those who wait risk being optimized out. The signal is already clear — and decisions are already being made.

👉️ Read the article here

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^{WORKFLOW BUILDER}
Rapid Course Launch Kit

Goal: Transform your expertise into a profitable digital course by using AI to handle the heavy lifting of curriculum mapping, lesson scripting, and slide design.

Steps:

1. Market-First Curriculum

   Validate your idea and map out a syllabus that students will actually pay for. Prompt ChatGPT: Write a comprehensive 6-module course outline for a program about [Insert Topic]. For each module, include 3 specific learning outcomes that solve a major pain point for [Insert Target Audience].
   ➡️ Next: Review the outline and select Module 1 to begin scripting.

2. High-Retention Lesson Scripts

   Convert your expertise into engaging video scripts that keep students focused.
   Prompt ChatGPT: Write a 10-minute video script for a lesson titled "[Insert Lesson Name]." Use the "Hook-Meat-Action" framework to ensure the content is actionable and ends with a specific homework task.
   ➡️ Next: Copy your script to prepare for visual production.

3. Rapid Slide Deck Creation

   Design professional, clean presentation slides without spending hours in design software.
   Prompt ChatGPT: Write a slide-by-slide breakdown for this script, describing exactly what text and visual metaphors should appear on each of the 10 slides to keep the audience engaged. 
   ➡️ Next: Use Guidde to magically turn your step-by-step instructions into stunning training videos 11x faster.

4. Interactive Student Assets

   Create the worksheets, checklists, and summaries that add "premium" value to your course.
   Prompt ChatGPT: Write a "Quick-Start Checklist" and a 5-question reflection quiz based on the content of the script provided in Step 2 to help students implement the lesson immediately. 
   ➡️ Next: Save these as PDFs to include in your course portal.

5. The "Sold-Out" Sales Page

   Draft the high-converting copy needed to turn visitors into enrolled students.
   Prompt ChatGPT: Write a long-form sales page for this course using the "Problem-Agitation-Solution" framework. Highlight the transformation students will experience and include 3 "frequently asked questions" with answers. 
   ➡️ Next: Paste your copy into your hosting platform and open enrollment.

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^{TODAY’S PROMPT}
Turn What You Learn Into a System You Can Use Daily

This mega-prompt turns scattered learning into a personalized knowledge system through a guided interview. It designs a custom capture, organization, and review workflow — helping you retain more, recall faster, and actually use what you learn instead of losing it in forgotten notes.

👉 Use the prompt + check out my example output here

CLEAR ASK
Act as my Knowledge Systems Architect.Your task is to interview me step by step to design a personalized knowledge system — a second brain tailored to how I learn, think, and work — and then produce a complete, ready-to-use system based entirely on my answers. Do not generate the system yet. First, run the interview.

CONTEXT & PURPOSE
Most people collect information endlessly but struggle to:
• Capture what actually matters
• Organize knowledge in a way they’ll reuse
• Recall insights when it counts
• Turn learning into output, decisions, or leverage
This interview is designed to:
• Understand how I consume, process, and apply knowledge
• Eliminate generic “productivity system” advice
• Design a system I’ll actually maintain and use
• Create a personal knowledge operating system, not a note dump
The final system should feel:
• Custom-built, not templated
• Simple enough to maintain daily
• Powerful enough to scale with long-term learning

INTERVIEW RULES (CRITICAL)
• You must ask questions sequentially
• Ask one section at a time
• Wait for my response before continuing
• Adapt follow-up questions based on my answers
• Do not skip ahead
• Do not assume tools, habits, or goals
• Keep questions clear, conversational, and human
• If an answer is vague, ask a clarifying follow-up before moving on.

INTERVIEW STRUCTURE
Phase 1: Learning Identity & Intent
Goal: Understand why I learn and what success looks like.
Ask questions to uncover:
• What I’m learning for (career, business, curiosity, leverage, creativity, etc.)
• Short-term vs long-term goals
• How I want knowledge to help me (make money, think better, create, decide, teach, etc.)
• Frustrations with past systems or note-taking attempts

Phase 2: Information Intake
Goal: Understand how information enters my world.
Ask about:
• Primary sources (articles, books, podcasts, videos, courses, conversations)
• Passive vs active learning
• How often I encounter information I want to save
• Where ideas currently get lost or forgotten

Phase 3: Capture Behavior
Goal: Design a friction-light capture workflow.
Ask about:
• How I currently capture ideas (if at all)
• Devices used (phone, laptop, tablet)
• Situations where capture fails
• Tolerance for friction (quick notes vs structured input)

Phase 4: Organization Preferences
Goal: Decide how information should be structured.
Ask about:
• Preference for folders vs tags vs minimal structure
• Whether I think top-down (categories) or bottom-up (ideas)
• How much organization I’m realistically willing to maintain
• Past systems that felt too rigid or too messy

Phase 5: Recall & Review
Goal: Make knowledge usable, not stored.
Ask about:
• How often I revisit old notes (if ever)
• Whether I prefer scheduled reviews or contextual recall
• Situations where recall matters most (writing, work,  conversations, decisions)
• Willingness to do weekly/monthly reviews

Phase 6: Output & Application
Goal: Tie knowledge to real-world use.
Ask about:
• How I want to use what I learn (write, build, teach, decide, create content, etc.)
• Whether learning should feed projects or remain exploratory
• Pain points around “I know this, but I can’t use it”
• Desired outputs from the system

Phase 7: Tools & Constraints
Goal: Keep the system realistic.
Ask about:
• Tools I already use (Notion, Apple Notes, Obsidian, Google Docs, etc.)
• Tools I want to avoid
• Time available per day/week
• Preference for simple vs powerful systems

FINAL OUTPUT (ONLY AFTER INTERVIEW IS COMPLETE)
Once the interview is finished, generate one complete system with the following sections:
1. Learning Capture Workflow
• Exactly when and how information is captured
• Rules for what gets saved vs ignored
• Low-friction defaults

2. Knowledge Organization Structure
• Clear structure (categories, tags, databases, or hybrid)
• Example entries
• Naming conventions and rules

3. Recall & Review System
• Daily / weekly / monthly touchpoints
• How knowledge resurfaces naturally
• How to avoid forgetting without over-reviewing

4. Output & Application Loop
• How learning turns into action
• How notes connect to projects or decisions
• How insights compound over time

5. Daily & Weekly Habits
• Simple routines to maintain the system
• Time expectations
• What to do when life gets busy

QUALITY STANDARDS
• Before delivering the final system, ensure:
• A beginner could follow it without confusion
• The system fits my actual behavior, not an idealized version
• Nothing feels unnecessary or overbuilt
• The result feels like a personal operating system, not advice

START THE INTERVIEW
Begin with Phase 1. Ask your first question only.

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^{AI SPOTLIGHT}
TSMC Is Cementing Its Role as the AI Backbone

TSMC’s record quarter confirms where real AI leverage lives: advanced chip manufacturing. Profit surged as demand for cutting-edge processors from AI leaders like Nvidia and AMD continues to dominate its business, pushing advanced nodes to the majority of revenue. While consumer electronics remain uneven, AI-driven compute demand is strong enough to offset weakness elsewhere. With aggressive investment in 2nm production and global expansion, TSMC is positioning itself as the indispensable supplier powering the AI stack.

Key Takeaways:

• AI chips now drive the majority of TSMC’s revenue
• Advanced nodes are becoming the economic center of semiconductors
• Massive capex reflects confidence in sustained AI demand
• Manufacturing advantage is concentrating power upstream

My Take:

This isn’t just a good quarter — it’s structural dominance. As AI workloads scale, the bottleneck isn’t software hype but who can reliably produce the most advanced silicon. TSMC isn’t riding the AI wave; it’s quietly controlling it.

👉️ Read the full article here

^{AI QUICK HITS}

⚡️ OpenAI is partnering with Cerebras to add 750MW of ultra-low-latency AI compute, aiming to dramatically speed up real-time responses for inference-heavy tasks like coding, agents, and complex queries as it scales interactive AI through 2028.

🏢 Microsoft is shutting down its internal library and canceling many news and research subscriptions as it pushes employees to rely on AI-powered learning tools instead of traditional books and publications.

🤔 ^{Which image is real?}

One of these images is real, and one was created with AI. Vote for which one you think is real, and tell us why you voted that way.

The last edition’s results—80% were right!

Did you vote in the poll from January 14th? The top image was real! Go back and see the photos here. So what do you think? Are you getting better at spotting AI vs. not?

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