^{AI MARKET PULSE}
Washington Is Quietly Debating an AI Job-Loss Safety Net Before the Shock Hits

A growing group of economists, investors, and former policymakers is pushing Washington to build an AI-era safety net before labor disruption turns into a full-blown crisis. Axios reports that BlackRock CEO Larry Fink warned this week that AI could widen wealth inequality, while former Commerce Secretary Gina Raimondo argued for a new public-private bargain built around training, job pathways, and stronger worker supports. One proposal would tie corporate taxes to how much output companies generate with shrinking headcount, while a separate “circuit breaker” framework would trigger wage insurance and expanded income support if labor-market stress spikes. The deeper thesis: the AI debate is moving beyond productivity gains and into a harder question—who captures the upside if employment takes the hit first?

So what does that mean for you?

The takeaway here is not to panic. It is to pay attention to where this conversation is going. If policymakers, investors, and major executives are already gaming out labor displacement scenarios, workers and business owners should be doing the same. In practical terms, that means:

• Build skills that make you more useful alongside AI, not easiest to replace
• Watch for industries where headcount may shrink even as output rises
• Treat adaptability and portability as career advantages now, not later

👉️ Read the article here

What if you could build a 5,000-page website without writing a single word?

A fitness website needed a page for every exercise in existence — squats, deadlifts, lunges, thousands more. They defined the exact structure each page should follow: specific fields, specific options, even generated images.

The AI produced every exercise page to that exact structure — not a block of text, but fully structured content matching the defined format for every exercise. At scale, with no duplicates.

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^{WORKFLOW BUILDER}
The Proposal Builder

Goal: Turn a messy client inquiry into a clean, professional proposal you can send the same day—and actually close.

Steps:

1. Extract the Client Brief
   Take the raw inquiry and turn it into a clear summary of what the client actually wants.
   • Prompt ChatGPT: Summarize this client inquiry into: goals, deliverables, timeline, budget signals, and key constraints. Highlight anything unclear or missing. [Paste client message]
   • ➡️ Next: Review and fix any gaps before moving forward.

2. Define the Scope of Work
   Translate the summary into a structured scope with clear deliverables and boundaries.
   • Prompt ChatGPT: Turn this summary into a scope of work with: deliverables, what's included, what's not included, and key assumptions to prevent scope creep. [Paste summary]
   • ➡️ Next: Adjust anything that feels too vague or too broad.

3. Structure the Offer
   Package the work into a clean, easy-to-understand engagement.
   • Prompt ChatGPT: Organize this scope into a proposal structure with phases, timeline, and deliverables for each stage. Keep it simple and client-friendly. [Paste scope]
   • ➡️ Next: Make sure the flow feels logical and easy to follow.

4. Generate Pricing Options
   Create pricing that aligns with outcomes, not just effort.
   • Prompt ChatGPT: Create 3 pricing tiers (basic, standard, premium) for this proposal. Tie each tier to scope and outcomes, and recommend one option. [Paste structured offer]
   • ➡️ Next: Sanity check pricing based on your market and confidence.

5. Write the Final Proposal
   Turn everything into a polished, send-ready document.
   • Prompt ChatGPT: Write a client-ready proposal including: intro, problem, solution, scope, timeline, pricing, and a clear CTA to move forward. Keep it concise and professional. [Paste full context]
   • ➡️ Next: Copy into Notion/Google Docs and send within 30 minutes.

Pro Tip (Upgrade):
Use Unbounce to drop your proposal into a clean, branded client page instead of a plain doc. It makes the offer feel more premium, gives the client a smoother review experience, and helps you present pricing and next steps more clearly.

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^{TODAY’S PROMPT}
The Offer Architect

This mega-prompt acts like a business strategist and offer designer, turning your rough skill, service, or product idea into a clear, sellable offer. It builds your positioning, offer stack, pricing logic, and objection handling, then packages everything into a simple landing page outline so you can go from idea to something you can actually sell—fast, without overthinking it.

👉 Use the prompt + check out my example output here

You are an expert business strategist and offer designer. Your job is to take a rough skill, service idea, or product concept and turn it into a clear, compelling, and sellable offer.

Start by asking me clarifying questions if needed. Then build a complete offer system using the structure below.

First, define the **Core Offer Positioning**:
• Who this is for (specific audience, not broad)
• The painful problem being solved
• The desired outcome or transformation
• Why this solution is different or better than alternatives

Next, design the **Offer Stack**:
• The main deliverable (what they actually get)
• 3–5 supporting components that increase perceived value
• Format (done-for-you, done-with-you, digital product, hybrid, etc.)
• Timeline or delivery structure

Then create the **Pricing Logic**:
• Recommended price point
• Why this price makes sense based on outcome/value
• Optional tiering (good/better/best if applicable)

Now identify **Objections and Friction Points**:
• List the top 5 reasons someone would hesitate to buy
• Provide a clear counter or reassurance for each

Then outline a **Simple Landing Page**:
• Headline (clear outcome-driven promise)
• Subheadline (who it’s for + why it matters)
• Key benefits (3–5 bullets)
• Offer breakdown
• Social proof ideas (even if hypothetical)
• Call-to-action

Finally, suggest **Ways to Strengthen the Offer**:
• Add-ons, guarantees, urgency, or positioning tweaks that would make this easier to sell

Keep everything clear, specific, and practical. Avoid fluff. Make the offer feel immediately usable and ready to test.

Ask me for my idea, skill, or product concept to begin.

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^{AI SPOTLIGHT}
Arm Is Making a Bigger Bet Than Just Another AI Chip

Arm just introduced its first in-house AI data center chip, the AGI CPU, marking a major shift for a company that has historically made money by licensing designs rather than building its own hardware. The chip is aimed at the fast-growing agentic AI market, where systems are expected to act with minimal human oversight instead of simply answering prompts. Meta is the lead partner on the design, TSMC is manufacturing it on a 3-nanometer process, and Arm says the product could generate roughly $15 billion in annual revenue within five years. That is a huge jump for a business Wall Street expects to do about $4.9 billion in revenue this fiscal year. The deeper thesis: Arm is no longer content to sit in the background of the AI boom. It wants to move closer to the center of the stack and capture a much bigger share of the upside.

Key Takeaways:

•Arm’s AGI CPU is its first major move from IP licensing into selling its own AI chip
•The company is targeting agentic AI, one of the clearest next demand waves in data center compute
•If Arm can ship on schedule, this could reshape its role from supplier to direct AI infrastructure player

👉️ Read the full article here

^{AI QUICK HITS}

📽 OpenAI has reportedly shelved Sora, its AI video tool, in a move that startled Disney and signals the company may be narrowing its focus as the race to scale profitable AI products gets more intense.

🧑‍💻 Anthropic is giving Claude Code more autonomy while still keeping guardrails in place, underscoring where the market is heading next: coding tools that act more like junior operators than simple assistants.

🏭 Bernie Sanders and Alexandria Ocasio-Cortez have introduced a bill to pause new data center construction, bringing the energy, labor, and infrastructure backlash to the center of the AI buildout story.

🤔 ^{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—52% were right!

Did you vote in the poll from March 25th? 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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