We analyzed 100+ videos from 3 top AI creators to extract what actually works: service offerings that sell, pricing models, tool stacks, and client acquisition strategies.
AI Jason puts it simply: "99% of developers are exploring AI agents in 2025." The concept of autonomous agents that can break down goals, execute tasks, and iterate on results has moved from research projects to production-ready businesses.
The shift is clear in the data: AI workflow automation is projected to hit $630B by 2028. But more importantly, the barrier to entry has dropped dramatically. Tools that required teams of engineers a year ago can now be built by solo developers in days.
Real revenue example
AI Jason documents a case study of going from $0 to $4M with just 2 people using ComfyUI for e-commerce AI services. The key: targeting a specific vertical (e-commerce product photography) where AI can replace expensive traditional processes.
The opportunity isn't in building general-purpose AI tools. It's in building vertical AI agents: software designed to complete specific tasks within particular industries. As AI Jason explains, "The fundamental pattern is creating an agent with access to various tools and assistants, plus a specialized playground for users to review and collaborate."
Based on analysis of 277 videos, these are the AI automation services with proven demand and revenue.
Autonomous agents that handle outreach, follow-up, and meeting booking. AI Jason built a sales BDR agent that handles the entire pipeline from research to booking.
Caution: One agent accidentally talked to another, creating an infinite loop that cost $5,000 in API calls on a single Friday.
Voice agents for cold calling, customer support, and interviews. Your Average Tech Bro built a mock interview tool using LiveKit that conducts real-time conversational interviews 24/7.
Key insight: Use the speech-to-text pipeline instead of real-time models for significantly lower costs.
Universal scrapers that can handle any website structure. LLMs enable extracting structured information from raw HTML without custom parsing for each site.
Two approaches: API-based (cheaper, simpler) or browser control (handles complex UIs, auth, pagination).
Building paid MCP (Model Context Protocol) servers for AI agents. AI Jason calls this "the next big opportunity" - a standardized way for AI agents to access external tools.
Revenue model: Usage-based pricing with Stripe Meters, subscription tiers, or one-time purchases.
AI model swapping, product photography, and visual content generation. In Chinese e-commerce, a large percentage of visual content is already AI-generated.
Use case: Replace models in product photos with different nationalities for international marketing at a fraction of traditional costs.
Multi-agent systems for research, writing, and editing. Using frameworks like Autogen to create pipelines where research agents feed content writers who are reviewed by editors.
Architecture: Separate group chats for research (search + scraping) and writing (editor + writer + reviewer).
AI Jason's deep dive on "The REAL cost of LLM" reveals that traditional subscription pricing often doesn't work for AI products. Here are the models that do.
| Model | How It Works | Best For | Example Pricing |
|---|---|---|---|
| Usage-Based | Charge per action, API call, or token consumed using Stripe Meters | MCP servers, API services | $0.01-0.50/action |
| Tiered Subscription | Monthly fee with usage limits per tier | SaaS products, ongoing services | $29-299/month |
| Project-Based | Fixed fee for building custom agents/workflows | Custom development, consulting | $2,000-50,000+ |
| Retainer + Usage | Base monthly fee plus variable usage costs | Managed services, ongoing support | $500-5,000/mo + usage |
Critical: Understand Your Costs First
AI Jason shares a cautionary tale of an AI companion startup that couldn't break even because unexpected usage made subscription pricing unsustainable. Before setting prices, instrument your LLM costs with tools like LangSmith to understand your true cost per user.
Extracted from 277 videos across all three creators. These are the tools they actually use and recommend.
Vercel AI SDK
Open-source, great for web apps with streaming
LangGraph
Best for flow engineering and reliable agents
CrewAI
Multi-agent systems with defined roles
Autogen (Microsoft)
Controllable agents with human feedback loops
Claude Code
Higher quality output, terminal-based workflow
Cursor
Better IDE integration, faster for quick edits
SweetPad
Turn Cursor into a proper iOS IDE
LiveKit
Same tech OpenAI uses for voice mode
VAPI
Managed voice AI for outbound calls
Groq
LPU for insanely fast inference
Supabase
Auth, database, storage in one
Stripe Agent Toolkit
Usage-based billing for AI services
LangSmith
Observability and cost monitoring
AgentQL
Web scraping with browser automation
AI Jason's "Flow Engineering 101" identifies the core problem: models don't consistently follow prompts in production. Here's how top builders solve it.
Move from simple prompt-response to structured, iterative approaches. Break complex tasks into smaller steps where you control the high-level flow but let LLMs make decisions within each step.
AI Jason emphasizes the paradigm shift: instead of one agent doing everything, build systems of specialized agents. Benefits include easier updates, compounding capabilities, and reduced development costs through reusable components.
Implement retry mechanisms and error handling. When code generation fails, have the agent review the error, attempt a fix, and re-run. Autogen's framework enables this with conditional edges and feedback loops.
Use User Proxy Agents (from Autogen) to allow human feedback during execution. Don't let agents run completely autonomous for critical tasks - build in review points.
The LangGraph approach
LangGraph uses "nodes" (steps) and "edges" (connections) with conditional routing. For a SQL agent, you might have: list tables -> get schema -> generate query -> execute -> (if error, retry generation). The shared state maintains context across steps.
Matthew Berman highlights a sobering stat: 95% of AI pilots in enterprises fail according to MIT research. The core issues are trust and control. If you're targeting enterprise clients, AWS AgentCore addresses this with:
Define guardrails in natural language. Agents only access permitted resources with automatic code translation.
Assess agents using metrics like correctness, helpfulness, and faithfulness. Full observability to trace issues.
Agents learn from successes and failures across interactions. Memory propagates throughout the system.
Your Average Tech Bro built 14 apps over 5 years and shares exactly what works: "Only a few core strategies were consistently applied across my projects." Here are the four that actually drive results.
Create viral content with the product central to the story. Key advice: practice constantly, shamelessly copy successful formats, and ensure the product is visible. Their $1,500/month SaaS was built primarily through organic social following.
Strong community engagement and SEO benefits. Warning: be careful about self-promotion rules - you can get banned. Navigate subreddit rules carefully, but the power for user acquisition is significant when done right.
For B2B AI tools, cold outreach via Instagram and LinkedIn DMs is the primary growth strategy. Slower than viral social media, but allows for intimate user relationships and valuable feedback. This is how they're growing their career coach white-label product.
Generate thousands of pages targeting long-tail keywords. Your Average Tech Bro uses this for Perfect Interview: auto-generating practice questions and sample resumes from scraped job descriptions. Focus on high-intent keywords where users are ready to buy.
Paid ads reality check: $2,000 spent, 0.47 ROAS
Your Average Tech Bro spent $2,600 on Meta ads: 200K impressions, 2% CTR, $0.58 CPC. But average purchase was $39 with 0.47 return on ad spend - a loss. Lesson: paid ads are hard to make work when your product has high churn or low lifetime value. Focus on organic first.
Your Average Tech Bro pivoted Perfect Interview from B2C to B2B after realizing a fundamental problem: when users succeed (get a job), they churn 100%. The solution? White-label the technology to career coaches who have ongoing student relationships.
9 apps, 3 years: The lesson on founder-niche alignment
Your Average Tech Bro's Nexus Research.ai hit $450 MRR but they stopped working on it - they weren't passionate about the student/research niche. Their advice: assess if you want to serve this audience for years, not just if the market exists. Product-founder fit matters more than product-market fit.
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The most in-demand services are AI sales agents, web scraping, workflow automation, MCP server development, voice AI agents, and e-commerce AI solutions like product photography automation.
Pricing varies widely: usage-based models start at $0.01-0.50 per action, monthly retainers range from $500-5,000/month, and project-based work can range from $2,000-50,000+ depending on complexity.
Common tools include Vercel AI SDK, LangGraph, CrewAI, and Autogen for agent development; Cursor and Claude Code for AI-assisted coding; LiveKit for voice AI; and Stripe Agent Toolkit for billing.
Top creators recommend building demo projects that showcase results, creating organic social media content, targeting specific industries with proven use cases, and finding co-founders with distribution skills.
Use flow engineering instead of just prompts, build systems of specialized agents instead of one super agent, implement self-healing loops with retry mechanisms, and add human-in-the-loop checkpoints for critical tasks.
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