We agree on the problem. We agree on the mantra. This doc explores four product shapes this could take — what each looks like, what it takes to build, and who it serves. The goal: pick a direction.
AI is moving fast. Any static content we create is outdated within weeks. So we don't create content — we curate, aggregate, and personalize it. The internet is already drowning in AI tutorials, YouTube walkthroughs, tool reviews, and prompt guides. The problem isn't supply. The problem is: none of it is organized around what you actually do for a living.
A real estate agent doesn't care about "prompt engineering." They care about writing listings faster. A CPG brand manager doesn't need "AI fundamentals." They need to know which tools analyze shelf data. The value isn't the information — it's the filter.
Our job is to be that filter. We use AI to watch the entire ecosystem, pull in what's relevant, and serve it to people based on their profession. Humans handle marketing and distribution. AI handles everything else. That's how this scales.
Weekly walkthroughs of AI tools for listing creation, CMA reports, and lead nurturing
Top-producing agent sharing daily AI workflows — listing descriptions, follow-ups, market analysis
Breaking AI news filtered for real estate — tools, use cases, and industry-specific prompts
Bi-weekly email focused on AI for residential real estate professionals
Here's the unlock that makes this grow fast without paid acquisition. The creators and voices we feature in the tool have every incentive to promote us — because we're sending them followers.
The deal is simple: we trade placement for promotion. "We'll feature you as a top voice for AI in real estate — share it with your audience." Every creator who shares their Teach Me listing is driving their followers to our platform, where those followers enter their own profession and discover more creators, who then share it with their followers.
This is how you grow a user base without a marketing budget. The creators ARE the marketing. And the bigger our user base gets, the more valuable the placement becomes, which means bigger creators want in, which brings more users. It compounds.
The pitch to creators: "Teach Me recommends you to every [profession] who asks who to follow for AI. Last month we sent 2,000 professionals to creators in your space. Want to be on the list? Share your listing and we'll feature you." As the platform grows, this becomes a premium placement opportunity — another revenue stream.
Phase 2: The Newsletter (Weeks 2-6). Once the micro-tool is capturing emails (segmented by profession), launch a weekly newsletter. Week one: updated "who to follow" list plus highlights from those creators. Week two: curated content — "the best thing we saw this week for real estate agents using AI." Week three: add a prompt template. Week four: tool recommendation. Each email progressively deepens the value while training people to open. The creator flywheel keeps feeding the top of the funnel while the newsletter builds the relationship.
Phase 3: The Paid Product (Months 3-5). You now have an email list segmented by profession, engagement data on what content resonates, and creator relationships in multiple verticals. Pick whichever path (A, B, C, or D) the data points to and launch it to your warmest users first. The newsletter audience is your built-in beta group — they already trust you, they already gave you their profession, and they've been primed by weeks of free value. "We're launching something new — reply if you want early access" to your most engaged subscribers.
Phase 4: Layer and Expand (Months 6+). Add more paths as the data justifies them. Convert newsletter subscribers to paid. Expand to new professions using the same playbook: curate creators, launch the micro-tool for that vertical, build the newsletter, promote the paid product. Each new profession is a repeat of the same formula, not a reinvention.
Why this sequence wins: You never have to convince a stranger to buy something. By the time you launch a paid product, your audience has already received weeks of free value, they've self-selected by profession, and they've been warmed by creators they trust. The micro-tool → newsletter → paid product pipeline means every dollar of revenue comes from someone who already knows you're useful.
One real work scenario for your profession, every day. "You just got a brief for a new product launch. Use AI to generate a competitive landscape in under 10 minutes. Go." Takes 5–10 minutes, builds muscle memory, and creates a Wordle-style shareability loop. Streaks keep people coming back.
A personal "AI Quotient" — a score that quantifies how AI-fluent you are in your profession. Think credit score for AI skills. Vanity metrics drive engagement, and a visible score gives people something to improve — and something to put on LinkedIn.
Duolingo proved that streaks are one of the most powerful retention mechanics in consumer software. A "days active" streak combined with profession-specific leaderboards ("Top 10% of brand managers this month") creates both internal motivation and social proof.
Gate certain premium content behind sharing. "Share your weekly briefing with a colleague to unlock this month's advanced prompt library." Every user becomes a distribution channel — and they look good sharing useful AI insights.
A weekly summary showing how much time you've saved (or could have saved) by using AI. "This month: 6.5 hours saved across 4 workflows." Quantified ROI is the strongest argument against cancellation — and a concrete number to tell your boss.
Anonymous benchmarks showing how your AI adoption compares to others in your field. "72% of brand managers in your cohort are using AI for competitive analysis — you haven't tried this yet." Social proof meets FOMO.
1. Which 5-10 professions do we curate creators for at launch? Start with content/marketing, CPG, and real estate — what else?
2. How many creators per profession do we need to make the tool feel valuable? 8? 12? 20?
3. Do we DM creators before launch to lock in the flywheel, or curate first and pitch them after we have traffic?
4. Newsletter platform: Beehiiv (built for growth/monetization) vs. ConvertKit (built for creators) vs. something else?
5. At what list size do we launch the paid product? 500 subscribers? 1,000? 5,000?
6. The YouTube wrapper idea — does this become a feature of the newsletter (embedded video highlights) or a standalone section of the tool?
7. Which of the four paths (A-D) does your gut say becomes the paid product?
1M+ subscribers. Free daily email, 3-minute read covering AI news, tools, and tutorials. Founded by Zain Kahn, 1.5M+ social following. Pro tier offers weekly deep-dives.
The problem: Same email for everyone. A real estate agent and a CPG brand manager get identical content. Zero personalization by profession or role. It's a broadcast, not a filter.
Other large AI newsletters (600K+ and 500K+ subscribers respectively). Similar model to Superhuman — daily AI news, general audience, no profession-based personalization.
The problem: Same as Superhuman. Great for awareness, useless for "how do I use this in my specific job?"
Structured AI courses (live + on-demand), Slack community, expert instructors from OpenAI/Google/Meta. Consulting services for enterprise.
The problem: Generic courses ("AI for Writing," "AI Crash Course") — not "AI for your specific role." Content goes stale. On the creation treadmill.
Massive course libraries from universities and industry experts. AI-powered recommendations, skill tracking, analytics dashboards. Enterprise-focused pricing.
The problem: AI is one category among thousands. Not purpose-built for AI upskilling. Courses are static, long-form, and generic. Individual professionals aren't the target buyer.
AI-powered skill intelligence platform. Aggregates skills data across HR systems, creates personalized learning paths. ~$10-15/user/mo for enterprise contracts. Implementation costs $2K-$20K.
The problem: Broad skill-development platform, not AI-specific. Sells to HR/L&D, not individuals. Inaccessible to the solo professional or SMB.
Enterprise LXPs with AI personalization engines, skill assessments, and content libraries. Pluralsight is tech-focused; 360Learning is collaborative; Sana uses generative AI for course creation.
The problem: Six-figure enterprise contracts. Built for L&D departments, not individuals. Broad learning platforms that happen to include some AI content.
Free training tied to learning a specific vendor's product. HubSpot teaches HubSpot AI features. Salesforce teaches Einstein. Google teaches Gemini. Useful but narrow — they're selling you their tool, not teaching you AI.
The problem: Vendor lock-in by design. They teach you their AI, not how AI applies to your work broadly. And they don't care what you do for a living.
Every competitor organizes by topic ("prompt engineering") or by tool ("learn ChatGPT"). Nobody organizes by who you are and what you do. That's the whole product.
SectionAI, Coursera, Udemy — they're all on the content creation treadmill. Their stuff goes stale. We curate from the entire internet and are always current by default.
Free newsletters → $500+/yr courses → six-figure enterprise LXPs. There is nothing in the $10-20/mo range for an individual professional who wants more than a newsletter but won't pay for courses.
Path D — contextual AI coaching inside your actual tools — has zero competitors. The closest analogy is GitHub Copilot for developers. Nobody has built this for knowledge workers.
Our edge isn't content. Our edge is distribution + personalization. We're not going to out-teach SectionAI or out-write Superhuman. We're going to out-target them. Same internet, better filter, delivered to the right person at the right time in the right context.
A personalized AI advisor that lives in your pocket and knows your job. Maximum personalization, highest technical complexity, strongest retention potential if the AI is good. Weakest at passive engagement — requires the user to actively use it.
Upload a planogram or shelf photo and get competitive analysis in seconds. Here's how this changes your workflow →
@BrandAI_Mike walks through using AI for quarterly brand narratives — exactly your use case
"Given this pricing data for [category], identify the three biggest opportunities for..."
VP of Brand Strategy at [CPG Co], posts weekly AI workflows for brand managers
1. Paste your last product brief into Claude and ask for three consumer-facing angles you haven't considered
2. Use Perplexity to pull the latest market share data for your category — it's faster than your current process
A personalized weekly briefing that finds what matters for your specific job so you don't have to. Easiest to launch, easiest to explain, best unit economics. Weakest at depth and interactivity — it's consumption, not conversation.
Directly applies to what you're learning right now →
Preview of Module 4 material — watch this 4 min video
A structured, personalized learning platform that also stays current. Strongest value proposition and highest willingness-to-pay, but most complex to build. The risk is over-engineering before you've validated the market.
3 acted on · 2 dismissed · 2 snoozed
Based on tasks where AI could have helped
You don't need to build the full extension on day one. Start with something simpler — a "daily shadow report." The user describes what they worked on that day (or connects their calendar), and Teach Me sends a short end-of-day message: "Based on what you did today, here are 3 moments where AI would have saved you time, and exactly how." It's retrospective instead of real-time, but it trains the same muscle — and it's buildable in weeks, not months. The full browser extension becomes the premium upgrade once you've validated the concept.
An AI layer that lives inside your actual work tools and teaches you in the moments that matter. Hardest to build, stickiest to retain. The MVP version (daily shadow report) lets you validate the concept fast before investing in the full extension.
| Dimension | A · Career Coach | B · Personalized Feed | C · Learning OS | D · AI Shadow |
|---|---|---|---|---|
| One-liner | "An AI advisor that knows your job" | "A weekly AI briefing for your profession" | "Learn AI for your role, at your pace" | "AI coaching inside your actual work" |
| Core experience | Conversation | Consumption | Progression | Context |
| Engagement model | Pull (user initiates) | Push (content delivered) | Push + pull | Ambient (always on) |
| Personalization |
Infinite — any question
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Filtered by profession
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Adapts path + feed
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Knows what you're doing right now
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| Time to launch | 6–10 weeks | 3–5 weeks | 10–14 weeks | 4–6 weeks (MVP) 12+ weeks (full extension) |
| Content burden | Low | Lowest | Medium | Low — moment library, not courses |
| Price point | $15/mo | $10–12/mo | $18–25/mo | $20/mo |
| Margin per user | ~$10-13/mo | ~$10-11/mo | ~$15-22/mo | ~$14-17/mo |
| Biggest strength | Feels like magic | Effortless; top-of-funnel | Clear ROI; highest WTP | Stickiest; lowest churn |
| Biggest risk | "Just use ChatGPT" | Content fatigue | Over-build before validation | Privacy concerns; build complexity |
| B2B upsell |
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