10 Ways to Get Monthly Income (let AI work for you)

Core business premise (the “test” for each idea)

The speaker argues most AI monetization attempts fail because founders can’t clearly answer:

• Who the customer is
• Why the customer should pay

Each of the “10 ideas” is framed with the same four points:

• What you do
• Which AI tools you need
• Who pays
• Cost vs. earnings

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Playbooks / frameworks explicitly used or implied

• Idea validation checklist (per idea)

  • Customer identity + pain
  • Offer definition (“what exactly do you do”)
  • Tooling stack
  • Unit economics (tool/platform cost vs. revenue/pricing)

• Go-to-market sequencing

  • First 5: implement quickly (often solo, no-code)
  • Last 5: build scalable companies (more engineering/product/customer acquisition)

• Niche iteration vs. universal product

  • “Take idea 1/3/6 and specialize to one niche”
  • Claim: universal products face ~50,000 competitors vs specialized ~3

• Build for “the last 20%”

  • Tool demos deliver ~80% quickly
  • The remaining ~20% (e.g., calendar, payments, workflow) breaks most apps

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Key monetization ideas (with business execution details)

1) AI phone answering for local businesses (voice agents)

What you do

• Deploy an AI “secretary” that answers calls, records the caller, and schedules the appointment.
• Includes a quality agent fallback: if uncertain, it says “You will be called back within 10 minutes.” (Designed to avoid guessing → hanging up.)

AI/tools

• Described tool: Retail (template-driven)
• Platform buy of a phone number
• CRM connection for follow-up

Customer

• Local businesses needing call handling (service providers, implied)

Economics / pricing (concrete)

• Agencies charge $3,000–$5,000 for setup; $30,000+ with CRM + monthly subscription.
• Speaker’s cost estimate: $0.13–$0.30 per minute (platform + phone line + model).
• Example spend: 20 calls/day → ~$200–$400/month on support.
• Profit logic: client pays for development + guarantee, not just usage.

Case / evidence

• Example builder (“Harshit”):
  • Team of 5
  • Earns millions/year
  • Sells voice agents for $20k–$50k + subscription
  • First development: 2 months
  • Now iteration: 2–5 days
  • Still sets a 2-month deadline because the client buys “guarantee calls answered,” not time.

Operational caveat

• Ensure the AI doesn’t “guess”; quality gating is part of the offer design.

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2) Human AI evaluation marketplaces (“assess quality”)

What you do

• Test AI responses by rating/assessing quality.

AI/tools

• Platforms: Asmercor (speaker’s example), plus Scale AI, Aligner, Remotasks

Customer

• AI companies needing human evaluation data

Economics / KPIs

• Typical offers: ~$35/hour, increasing with specialization
• Work arrives in batches (not steady 40-hour throughput)
• Scale example claim:
  • Multicore: $640M revenue, 90% from AI companies paying for evaluation

Risk management

• Treat it like part-time (not predictable full-time).

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3) Automation for operations + financial reporting (Zapier-first, real-time events)

What you do

• Automate paperwork and internal workflows, e.g.:
  • Form submission → CRM record → Slack message
  • Eliminate manual copying

AI/tools

• Zapier and n8n emphasized
• Difference: n8n listens to real-time events (vs timers in some tools)
• Financial reporting example:
  • Upload receipts/checks → categorize → flag suspicious → monthly report

Customer

• Businesses with recurring admin + accounting-ish reconciliation

Key execution warnings

• “Don’t touch bank statements in the first month” (access risk outweighs quality risk)
• Speaker rationale: losing clients is tied to access, not model performance
• Example cited: layoffs at Block attributed partly to AI taking jobs; profits increased, and “those jobs become services you sell.”

Pricing/offer examples

• Not fixed pricing stated here, but a repeated pattern appears later: services $1–2k/month

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4) Content creation services using AI media tools (sell outcomes/taste, not raw generation)

What you do

• Create product images/videos/clips and ad-ready assets in a consistent style.
• Cut clips from long videos and produce voiceover clips.

AI/tools

• Image/product: ChatGPT (product photos)
• Video generators: Higgs Field, Open Art, InVideo
• Clip cutting: Opus Clip
• Voice: ElevenLabs (commercial license $5/month; free plan restrictions)

Pricing + cost example

• Case “Priyanko”:
  • Makes $1.5M/year creating video ads
  • Traditional ad production cited: ~$500k
  • Charges: $25k–$50k
  • Speaker claims cost to him: ~$2k

Marketing/offer positioning warning

• Avoid “dead” directions:
  • Don’t sell AI video ads as cheap mass product (basic generation is free in many tools)
  • Don’t build around dubbing (YouTube made it widely free)
• Thesis: paid creators win by selling taste and quality curation, not just generation.

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5) “Projects + skills” consulting/setup (highest hourly value in the list)

What you do

• Teach individuals/teams to use AI properly by building:
  • Projects: upload business context once (customers, style, assortment)
  • Skills: teach repeatable workflows so outputs stay consistent

Value proposition

• You solve the “you don’t know what you don’t know” gap.
• You’re not only delivering output—you’re saving time and reducing operational friction.

Pricing

• Individual consultation: $200–$500 per hour (or session)
• Team/full setup: $500–$2,000

Implied deliverable

• A customized hour or two that translates AI behavior into the client’s real processes.

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Scalable “build companies on it” ideas

6) Build AI apps fast using developer platforms (Google AI Studio → paid app)

What you do

• Create working applications using AI agent/app builders and sell them (or charge per project).

AI/tools + pricing ladder

• Google AI Studio: free
• Lovable: $25; charge $500–$2,000
• Replit / Cloud Code / Codex: ~$20; charge $2,000–$8,000
• Claim: each level effectively costs ~$20; what changes is where the app runs.

Critical execution warning (“last 20%”)

• 20-minute demos often equal prototypes.
• The hard parts: calendar, payments, real workflow

Case examples

• Agency “Imaginary Space”: $100k/month, minimum project $20k
• Shippit operators: €10k MVP pricing

Lesson

• Verification and delivering the full workflow beats impressive prototypes.

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7) Customer sourcing + outbound (Clay + Apollo + AI messaging)

What you do

• Provide a targeted list of real prospects and outreach trigger reasons.
• Sell “who to contact this week” rather than building heavy software.

AI/tools

• Clay: prospecting + enrichment
• Apollo: outbound communication
• ChatGPT/Claude: writing exploration and outreach messaging

Pricing logic

• If client value is $10k and you bring 3 customers, client accepts ~$2k/month
• Example maintenance:
  • $1,000–$5,000/month
  • 3 clients → $9,000/month

Positioning

• Framed as the fastest money offer because it directly drives revenue.

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8) Niche verticalization strategy + “AI phone agent” replication (Happy Robot)

What you do

• Take the phone-agent model (Idea 1) and specialize it deeply to a single vertical.

Example

• “Happy Robot”:
  • Answers “where’s my truck” for transportation companies
  • Raised $150M at $1.2B valuation
  • Backed by a16z
• Core strategy: general AI → niche AI → iteration

Execution principle

• Specialize because competition is dramatically smaller.

Additional niche case

• “Photo AI” by Peter Levels:
  • ~$105k/month
  • Operates with “no employees” and “a single PHP file”
  • Focused on one audience + one problem (AI photo versions)

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9) “Boring AI accounting” company (Basis) + trust as the moat

What you do

• Automate receipt ingestion and transaction categorization.
• Flag anomalies and output a monthly clean report.

AI/tools

• Approach mentioned: Claude + n8n (or ChatGPT + n8n)
• Flow: receipts → classify → flag → report

Customer

• Businesses needing monthly accounting/reporting automation

Pricing

• $1,500–$3,000/month per client

Scaling metrics

• 3 clients → ~$4,500/month recurring

Moat

• Trust + access control:
  • “Don’t touch bank flow in the first month”
  • “Start boring and stay boring; boring businesses are hardest to quit.”

Case evidence

• “Basis”:
  • Raised $100M at $1.15B valuation
  • Backed by Accel and Google Ventures

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10) Setup/implementation services for AI training budgets (done-for-you deployment)

What you do

• Don’t do ongoing labor yourself; set up the team to use AI.

Customer incentive

• Companies often have an approved training budget that must be spent by year-end.

Pricing + timeline

• Entire team setup: $2,500–$10,000 in a few days

Core GTM logic

• You win because you’re a turnkey implementation option during budget selection.

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AI prompt / selection mechanism (actionable, business planning)

The speaker provides a “secret prompt” used in Claude/ChatGPT:

• Asks 5 questions
• Selects 1–2 of the 10 ideas
• Generates a weekly plan for the first 30 days, including:
  • What to set up
  • What to skip
  • Who to contact
  • How much to charge

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Notable investing/market commentary (high level only)

• AI disruption is driving “service stock” valuation movement by implying software can replace staff (examples cited: Oracle job cuts; AI impact on IT services).
• This is used to justify choosing scalable business models (ideas 6–10).

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Presenters / sources mentioned

• Vaibhav (main speaker; referenced by name in subtitles: “Vaibhav”)
• Harshit (voice agent case)
• Jack Dorsey / Block (layoffs and profits example; founder reference)
• Josh Woodward / Gemini application at Google (podcast quote about “Taste” as the most important skill)
• Priyanko (video ads case)
• Imaginary Space (agency example)
• Shippit (MVP pricing example)
• Med (New York Times-described story; AI doctor accounts/FDA warning referenced)
• Happy Robot (raised $150M / a16z example)
• Peter Levels (Photo AI example)
• Basis (raised $100M / Accel + Google Ventures example)
• a16z (investor mentioned)
• Accel (investor mentioned)
• Google Ventures (investor mentioned)
• New York Times (referenced in “Med” story)

Tools/platforms referenced

ChatGPT, Claude, Retail, Asmercor, Scale AI, Aligner, Remotasks, Zapier, n8n, Google AI Studio, Lovable, Replit, Cloud Code, Codex, Clay, Apollo, ElevenLabs, Higgs Field, Open Art, InVideo, Opus Clip