Morning Coffee: Here's a stat that is interesting about AI
Only 4% of creators earn more than $100K/year.
For context: Before the AI revolution, more than 4% of the general workforce earned six figures from regular jobs — accountants, engineers, managers, salespeople.
So the AI revolution, built an entire "creator economy" — with all its promise of freedom, flexibility, and following your passion — and the result is worse odds of making a living than just getting a job.
Why this matters ?
Every AI tool that makes creation easier also makes competition fiercer.
When everyone can,
Generate videos in seconds (AI video)
Build apps without code (vibe coding)
Write content at scale (LLMs)
...the supply of "content" explodes. But demand doesn't scale the same way. Attention is finite. Wallets are finite.
The reductionist trap:
Innovation that only reduces cost — without creating new value or new markets — is a race to the bottom. If AI's primary contribution is "now everyone can do what used to require skill," the economic outcome is deflation, not growth. The printing press didn't just make books cheaper. It created entirely new categories: newspapers, novels, scientific journals, mass literacy.
That's expansionary innovation.
The question for builders:
Is your AI tool expanding the pie, or just giving more people a smaller slice? The 4% stat isn't a failure of creators. It's a warning about what happens when innovation optimises for production without solving distribution and monetisation.
Build tools that help people get paid, not just make things.nThat's where the real opportunity lives.
Growth Hack
AI-Powered Network Targeting: Find Your Biggest Opportunities and Draft the DMs
The problem: Your network is full of potential — collaborators, clients, partners. But you don't have time to analyze who matters most or craft personalised outreach for each one.
The solution: AI scans your network, identifies high-value targets in your niche, and drafts personalised DMs for you to approve and send.
HOW IT WORKS
Your niche + network data → AI identifies top influencers → Drafts personalized DMs → You approve → Send
The key insight: The best opportunities are already in your network (followers, following, engagement). You just haven't mapped them.
STEP 1: DEFINE YOUR NICHE FOR THE AI
Create a "niche profile" document:
NICHE PROFILE:
- I help: [WHO - be specific]
- With: [WHAT problem/outcome]
- My expertise: [YOUR unique angle]
- Ideal collaborator: [WHO would amplify your reach]
- Ideal client profile: [WHO buys from you]
- Keywords that signal fit: [TERMS they'd use in bio/posts]
- Red flags to avoid: [WHO is NOT a fit]
Example:
- I help: B2B SaaS founders
- With: Product-led growth strategy
- My expertise: Turning free users into paid (PLG mechanics)
- Ideal collaborator: Newsletter writers, podcast hosts in SaaS/growth space
- Ideal client: Series A-C SaaS with freemium model, 10K+ users
- Keywords: "PLG", "product-led", "freemium", "self-serve", "SaaS growth"
- Red flags: Agencies, "growth hacker", crypto projects
STEP 2: EXTRACT & SCORE YOUR NETWORK
Use a scraping tool (Phantombuster, Apify, or manual export) to pull:
Your followers list (with bios, follower counts)
People who engage with your posts (commenters, likers)
People you follow but haven't DMed
Then run through AI:
Prompt: "Score each person 1-10 for fit with my niche profile.
NICHE PROFILE:
[Paste your profile]
PERSON:
- Username: @[handle]
- Bio: [their bio]
- Followers: [count]
- Recent posts: [topics they post about]
Return:
- Score (1-10)
- Reason (one sentence)
- Outreach angle (what would you talk about?)
- Priority (high/medium/low)"
STEP 3: AI DRAFTS THE DM
For high-scorers, generate personalised outreach:
Prompt: "Write a DM from me to this person.
ABOUT ME:
[Your niche profile]
ABOUT THEM:
- @[handle]: [bio]
- They recently posted about: [topic]
- We have in common: [mutual followers, interests, etc.]
GOAL: [Start a relationship / Propose collaboration / Offer value]
RULES:
- Lead with something specific about THEM (not me)
- No "I'd love to pick your brain" or "Can I ask you a question?"
- Offer value before asking for anything
- Keep it under 50 words
- Sound like a peer, not a fan
- One clear next step
Write only the DM."
Example output:
"Your thread on PLG pricing tiers was sharp — especially the bit about anchoring to annual. I wrote a breakdown of how Notion structures their upgrade prompts that builds on your framework. Happy to send if useful."
STEP 4: BATCH APPROVE & SEND
Set up a simple Notion or Google Sheet:
Handle | Score | Outreach Angle | Draft DM | Status |
|---|---|---|---|---|
@founder_jane | 9 | PLG pricing | "Your thread on..." | ✅ Approved |
@saas_mike | 8 | Freemium metrics | "Saw your post..." | ⏳ Pending |
Review 10-15 DMs in 5 minutes. Edit if needed. Send.
WHY THIS WORKS
Most creators spray generic DMs hoping something sticks. This system:
Targets the right people — AI filters for actual fit, not just follower count
Personalizes at scale — Each DM references something specific about them
Leads with value — You're offering, not asking
Keeps you in control — Nothing sends without your approval
The math: 10 highly-targeted DMs to the right people beats 100 generic DMs to random followers. One real relationship with an aligned influencer can 10x your reach.
COST: ~$5/month (Claude API for scoring + drafting) TIME: 30 min/week (review and approve) RESULT: Consistent pipeline of warm relationships with people who actually matter for your growth
Daily Stat
$47B → $107B+ by 2028
That's the projected growth of AI in marketing tools. That’s a ton of marketing tools from AI. Not sure there is that much new in marketing to solve for? Look closer and you'll see what's actually happening: AI monetisation has concentrated almost entirely in content creation and content search.
Write faster. Generate images. Produce videos. Search smarter. Summarize documents.
Creation. Creation. Creation.
Here's the problem: Creation was never the bottleneck. Good creators always found a way to make things. The hard part was always distribution — getting the right people to see your work, trust you, and pay you. AI doesn't solve distribution. It solves repeatable format generation at scale.
What this creates:
More content chasing the same finite attention. More tools chasing the same creator FOMO. More startups that won't survive because they're all solving the same problem . The $107B market is real. But it's a market built on helping people make more stuff when the actual pain is getting stuff seen and monetised.
The nuance:
This isn't AI's fault. It's where the technology is easiest to apply. Distribution — understanding context, building trust, matching supply to demand — is harder.But that's exactly why the opportunity is there and thats where AI an truly unlock the economy
The shift that matters:
AI that helps you create → commodity (everyone has it) AI that helps you distribute → differentiation (few have cracked it) AI that helps you monetize → real value (almost no one is building this well). We've given everyone tools to make things. We haven't given them tools to make a living.
That's the gap. That's where the next wave of AI value will concentrate — or should.
Tool Tip
What it is: Ant Group's multimodal AI assistant that generates working apps, 3D visuals, and real-time scene analysis from natural language prompts.
Why it matters now:
LingGuang hit 1 million downloads faster than ChatGPT or Sora. Within two weeks, users created 3.3 million "Flash Apps" — functional mini-applications built entirely through conversation.
This isn't another chatbot. It's a personal AI developer in your pocket.
The killer features:
1. Flash Apps (30-second app generation) Describe what you want, LingGuang writes the code, builds the UI, and runs it — all in under 30 seconds. Users have created:
Workout planners
Expense trackers
Pac-Man-style games
Chinese character memorization tools
Trip planners with interactive maps
2. AGI Camera Point your phone at anything. LingGuang analyzes the scene in real-time — identifies objects, suggests edits, generates new content from what it sees. Snap a coffee menu, get instant comparisons. Film a whiteboard, get structured notes.
3. Multimodal Outputs Ask a question, get back:
3D models
Interactive charts
Animations
Maps
Working code
Not just text. Visual explanations that actually help you understand.
How it compares:
Feature | LingGuang | Cursor | Lovable |
|---|---|---|---|
Code generation | ✅ | ✅ | ✅ |
Instant execution | ✅ | ❌ | ✅ |
3D/visual output | ✅ | ❌ | ❌ |
Camera/scene analysis | ✅ | ❌ | ❌ |
Mobile-first | ✅ | ❌ | ❌ |
The catch:
Requires Chinese phone number or Apple ID for registration
Interface primarily in Chinese (though outputs work in English)
Server strain during peak hours (growing pains from viral adoption)
Generated code quality varies — still needs human review
What it signals:
China's AI ecosystem isn't just catching up — it's experimenting with entirely different form factors. While Western tools optimize for desktop developers, LingGuang bets that everyone becomes a developer when the interface is conversational and mobile.
Ant Group's CTO puts it simply: "We're giving everyone their own personal AI developer — someone who can code, create visuals, build programs, and turn complex ideas into simple solutions."
Pricing: Free (for now)
Best for: Non-technical founders who want to prototype fast. Educators creating visual explanations. Anyone curious about what "vibe coding" actually feels like.
Try it: lingguang.com (iOS App Store, Android stores, or web)
Ticker Watch
Rigetti Computing (RGTI) — The Quantum-AI Play That's Up 2,600% Since the AI Revolution Began
sPrice: ~$23 | Market Cap: ~$7.5B | 52-Week Range: ~$1 - $56
What they do: Build quantum computers and superconducting quantum processors, accessible through cloud infrastructure. Vertically integrated — they manufacture their own chips at their Fremont, California foundry.
Why investors care:
Quantum computing isn't just "faster computers." It's a fundamentally different architecture that could enable AI algorithms today's GPUs simply cannot handle. Drug discovery, financial modeling, cryptography, logistics optimisation — problems that take classical computers years could take quantum systems hours.
Recent catalysts:
Wedbush initiated with "Outperform" rating, $35 price target — citing leadership in superconducting qubits
Mizuho also "Outperform" — positioned Rigetti as peer to IBM and Google in quantum
$5.8M U.S. Air Force Research Laboratory contract for superconducting quantum networking
$5.7M in Novera™ QPU orders, delivery targeted H1 2026
2026 roadmap: 150+ qubit system with 99.7% median two-qubit gate fidelity
The thesis:
This is picks-and-shovels for the quantum revolution. While Google and IBM build quantum systems for internal use, Rigetti wants to be the TSMC of quantum — manufacturing chips for everyone else. Their U.S.-based foundry adds geopolitical protection in a technology that's increasingly seen as strategic infrastructure.
The bull case:
McKinsey estimates quantum computing adds $2 trillion in economic value by 2035
Rigetti has working hardware, real contracts, and a clear technology roadmap
Vertical integration (design + manufacturing + software) creates defensible moats
If quantum-AI hybrid systems work, Rigetti's full-stack approach wins big
The bear case:
Commercially viable quantum computers are still 4-5 years away (per IBM, Google)
Burning ~$21M/quarter with revenue at just $10.79M annually
P/S ratio of 856 — priced for perfection in an industry that's still experimental
Q3 2025: Revenue declined 18% YoY, operating losses at $200M+
Director just filed to sell 60K shares — insider confidence question
The reality check:
Rigetti's stock has risen 2,600% during the AI revolution. That's not based on fundamentals — it's based on narrative. The company is pre-revenue in any meaningful sense, burning cash, and competing against tech giants with unlimited R&D budgets.
But that's true of every frontier technology bet. The question is whether quantum computing follows the AI trajectory (explosive adoption once a threshold is crossed) or the fusion energy trajectory (always 10 years away).
Analyst consensus: Buy, with average target of $40.38. But analysts also thought Cisco was worth $555B in 2000.
My take: This is pure speculation on a real technology. If you believe quantum computing is 3-5 years from commercialisation and Rigetti survives the journey, there's 3-5x from here. If timelines slip or a competitor leapfrogs them, this goes to $5. Position accordingly
Not investment advice. Do your own research.
Daily Prompt
The "Strategic DM" Drafting Prompt
Use this to generate personalised outreach:
Write a DM from me to this person.
ABOUT ME:
- I help [WHO] with [WHAT]
- My expertise: [YOUR ANGLE]
- What I can offer: [VALUE YOU BRING]
ABOUT THEM:
- Handle: @[USERNAME]
- Bio: [THEIR BIO]
- They recently posted about: [TOPIC]
- Mutual connection: [IF ANY]
GOAL: [Start relationship / Propose collaboration / Offer value]
RULES:
- First sentence must be about THEM, not me
- Reference something specific they said or did
- Offer value before asking for anything
- No "pick your brain" or "quick question"
- Sound like a peer, not a fan or salesperson
- Under 50 words
- One clear next step (not "let me know if interested")
Write only the DM, nothing else.
Pro tip: The best DMs feel like the start of a conversation, not a pitch. If you wouldn't say it at a conference, don't send it.
Workflow
Webhook → Churn Save: One Signal, One Action
What it does: When a user's engagement drops, automatically send a personalized re-engagement email before they churn.
THE SETUP (n8n or Make.com)
User inactive 7 days → Webhook fires → AI writes personal email → Email sends
Step 1: Your app fires the webhook
// When user hasn't logged in for 7 days
await fetch('https://your-n8n.com/webhook/inactive-user', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
email: user.email,
name: user.firstName,
lastFeatureUsed: user.lastFeature,
daysInactive: 7
})
});
Step 2: n8n workflow
1. Webhook Trigger → receives user data
2. AI Node (Claude):
"Write a 3-sentence re-engagement email for {{name}}.
They last used {{lastFeatureUsed}}. Be helpful, not salesy.
Subject line + body only."
3. Send Email (Resend/SendGrid):
To: {{email}}
Subject: {{ai_subject}}
Body: {{ai_body}}
That's it. One signal. One action. One outcome.
RESULT: Users who get a personalised nudge at day 7 are 3x more likely to return than those who get nothing.
COST: ~$5/month SETUP: 20 minutes
The Vault
Deep dive for paid members
The Algorithm That Doesn't Exist: Why AI Governance Needs a New Framework
The EU AI Act assumes you can identify, audit, and regulate "an algorithm." But what happens when that algorithm is actually a distributed assemblage of foundation models, API chains, and business data — designed by multiple parties who can't see each other's layers?
This 12-minute deep dive examines:
Why Mittelstadt's influential 2016 ethics framework breaks down with agentic AI
What the Mobley v. Workday case means for AI vendor liability (spoiler: "we're just a tool" is no longer a defence)
Why "bias" isn't a bug — it's what customers are actually buying when they ask AI to "learn from our data"
Four concrete actions to update governance for systems no one fully understands
If you're building AI products, advising companies on AI adoption, or just want to understand how regulation is evolving — this is the framework you need.
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