^{AI MARKET PULSE}
AI Is Accelerating Faster Than Jobs Can Adapt

AI isn’t just improving productivity — it’s compressing entire roles out of existence. Geoffrey Hinton, often called the “godfather of AI,” warns that 2026 will bring a new wave of job losses as AI capabilities double roughly every seven months. Tasks that once took minutes now span hours, and Hinton expects AI to soon handle software projects lasting months with far fewer humans involved. Call centers are already being replaced, and even skilled knowledge work is increasingly exposed as reasoning models advance faster than anticipated. Economists echo the concern, noting that growth and employment have already decoupled. This isn’t a slow transition; it’s an acceleration curve where AI outpaces the labor market’s ability to absorb displaced workers.

So what does that mean for you?

Waiting to see how AI reshapes your role is the risk. The same acceleration threatening jobs creates leverage for people who act early:

• Build AI-enabled services instead of relying on roles
• Create digital products that scale with capability growth
• Automate workflows others still do manually
• Turn skills into systems that generate independent income

AI is compressing years of capability into months. Those who move now compound alongside the tools; those who wait face shrinking timelines and rising competition. The next wave isn’t abstract or distant — it’s imminent, and it rewards builders who reposition before the curve steepens.

👉️ Read the article here

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^{WORKFLOW BUILDER}
The "Editor-in-Chief" Assistant

Goal: Reclaim 10+ hours of "deep work" per week by letting AI automatically schedule focus blocks, buffer times, and task deadlines around your meetings.

Steps:

1. Audit Your Energy

   Identify which hours of the day you are most productive to set your AI scheduling rules.

   Prompt ChatGPT: Write a list of 5 questions to help me identify my peak "deep work" hours and determine which recurring low-value tasks should be automated or moved.

   ➡️ Next: Open Motion or Reclaim.ai and set your "Work Hours" based on these energy peaks.

2. Define "Deep Work" Blocks

   Create protected slots in your calendar that the AI will defend from incoming meeting invites.

   Prompt ChatGPT: Write a 1-sentence description for a "Deep Work" calendar event that clearly signals to colleagues that I am unavailable for interruptions.

   ➡️ Next: Create a "Smart Block" in your anchor tool and set it to "Always Private" or "Busy."

3. Sync Your Task List

   Connect your to-do list so the AI can physically place your tasks into your open calendar gaps.

   Prompt ChatGPT: Write a prioritized list of my top 5 business goals for this week and estimate the hours needed to complete each one.

   ➡️ Next: Input these tasks into your AI calendar tool with their "Due Dates" and "Time Estimates."

4. Set "Meeting Buffers"

   Program the AI to automatically add breathing room between calls to prevent burnout.

   ➡️ Next: Enable the "Decompression Time" or "Buffer" setting in your tool to add 15 minutes of "Auto-Scheduled" breaks after every meeting.

5. Review the Weekly Flow

   Optimize the AI's first draft of your week to ensure it aligns with your top revenue-generating goals.

   Prompt ChatGPT: Write a 3-point checklist to audit my calendar for "Strategic Alignment" to ensure I'm not spending too much time on admin versus growth.

   ➡️ Next: Use the "Reschedule All" button to let the AI reshuffle your week for maximum efficiency based on your final priorities.

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^{TODAY’S PROMPT}
See What Your Competitors Miss — Then Automate Past Them

This mega-prompt analyzes your competitors through a strategic AI lens and turns their weaknesses into a clear execution advantage. It delivers a competitive landscape snapshot, pinpointed AI opportunity gaps, and a step-by-step playbook with specific tools and tactics you can deploy immediately. The output is practical, execution-first, and designed to help you outpace competitors on speed, efficiency, and scale—without buzzwords, theory, or generic AI advice.

👉 Use the prompt + check out my example output here

CLEAR ASK
Act as a Senior AI Strategy Analyst and Competitive Intelligence Consultant. Your task is to analyze my competitors and produce a clear, actionable AI advantage plan that shows how I can outperform them using AI-powered strategies, tools, and implementation tactics.

Do not provide generic advice. Every recommendation must be specific, practical, and tied directly to competitive advantage.

CONTEXT & PURPOSE
I want to use AI not as a buzzword, but as a strategic weapon to outperform competitors on speed, cost, quality, reach, or scale.

This analysis should:
• Reveal where competitors are vulnerable or inefficient
• Identify AI leverage points they are underusing or ignoring
• Show exactly how to deploy AI to gain a measurable edge
• Translate AI capabilities into real business outcomes, not theory
• Help me make faster, smarter decisions than my competition

Assume I want practical advantage, not long-term speculation.

DETAILED INPUT REQUIREMENTS
Use the following inputs. If any are missing, make reasonable assumptions and clearly state them before proceeding.
• Industry / Market:[Insert industry or niche]
• My Business / Role:[Company type, size, or role — e.g., solo founder, SMB, enterprise team]
• Primary Competitors:[List 3–5 competitors, companies, or archetypes]
• Competitive Battleground:[Where competition matters most — e.g., pricing, content, sales, customer support, speed, product quality]
• Customer Type:[B2B, B2C, enterprise, SMB, creator, etc.]
• Time Horizon:[30 days / 90 days / 6–12 months]
• Risk Tolerance:[Low / Moderate / Aggressive]
• Output Depth Preference:[Concise / Tactical / Deep-dive]

ADDITIONAL INSTRUCTIONS
Follow these rules strictly:
• Be direct and decisive — avoid hedging language
• Prioritize AI as a force multiplier, not a replacement buzzword
• Recommend specific tools, workflows, or systems where relevant
• Assume limited time and attention — optimize for high ROI actions
• Focus on execution speed and unfair advantages
• Do not repeat common AI clichés or surface-level trends

When recommending tools:
• Explain what the tool does
• Explain how it creates leverage
• Explain how to implement it quickly

OUTPUT REQUIREMENTS
Deliver the analysis in this exact structure:
1. Competitive Landscape Snapshot
• How competitors currently operate
• Where they are strong
• Where they are slow, manual, or outdated
2. AI Opportunity Gaps
• Specific areas competitors are underutilizing AI
• Processes still dependent on humans or legacy workflows
• Strategic blind spots AI can exploit
3. AI-Powered Advantage Strategies
For each strategy:
• What to implement
• Why it outperforms competitors
• AI tools or systems to use
• How it compounds over time
4. Execution Playbook
• First actions to take in Week 1
• What to automate, augment, or eliminate
• Metrics that signal early success
5. Defensive Moat & Scaling
• How to make this advantage harder to copy
• How to scale it as competitors react
• Where to reinvest gains for compounding edge

FINAL QUALITY BAR
The final output must be:
• Immediately usable without clarification
• Competitive, not generic
• Grounded in real-world AI application
• Focused on winning, not experimenting
• Clear enough to turn into a task list or SOP

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^{AI SPOTLIGHT}
AI’s Real Shift Is Openness — Not Raw Power

The AI landscape is changing faster than brand dominance can hold. While GPT-5 had a strong year, momentum is shifting toward open-weight models like Alibaba’s Qwen. Despite scoring lower than top U.S. models on benchmarks, Qwen and peers from DeepSeek, Moonshot, and MiniMax are gaining adoption because they’re good enough — and far easier to customize, host, and run locally. Chinese open models now see more downloads than U.S. ones on HuggingFace, with Qwen ranking as the second-most-used open model globally. Developers are prioritizing flexibility over marginal performance as smaller models prove capable across real-world tasks. As closed systems stumble, adaptability is becoming the true advantage.

Key Takeaways:

• Open-weight models are gaining faster adoption than closed systems
• Qwen’s rise is driven by usability, not benchmark leadership
• Developers favor models they can run and modify themselves
• Chinese AI models are rapidly closing the quality gap
• Control and flexibility are replacing raw performance as differentiators

My Take:

This isn’t about who has the best scores — it’s about who controls deployment. As AI becomes infrastructure, openness beats prestige. Teams that can fine-tune and ship on their own terms move faster than those locked into gated APIs. The next phase of AI will favor builders who don’t need permission.

👉️ Read the full article here

^{AI QUICK HITS}

🧑‍💻 Andrew Ng argues that while AI is advancing quickly and will keep creating massive productivity gains, today’s systems are still fundamentally limited, heavily manual to build, and far from replacing humans anytime soon—making AI a tool to amplify people, not eliminate them.

🧠 OpenAI is hiring a new Head of Preparedness to study and manage emerging AI risks—from cybersecurity and self-improving systems to mental health—as the company acknowledges that its models are becoming powerful enough to create real-world challenges that need proactive oversight.

🤔 ^{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—59% were wrong!

Did you vote in the poll from December 27th? The bottom 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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